Overview

Dataset statistics

Number of variables12
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory107.3 B

Variable types

Categorical6
Numeric6

Dataset

Description2017, 2018, 2019, 2020, 2021, 2022년 세목별 지방세 미환급금 금액/건수/유형/누적미환금액/누적미환급금액증감 등에 관한 자료입니다.
Author경상남도 거창군
URLhttps://www.data.go.kr/data/15079215/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
당해미환급금액 has unique valuesUnique
누적미환급금액 has unique valuesUnique
누적미환급금액증감 has 4 (12.9%) zerosZeros

Reproduction

Analysis started2023-12-12 12:32:58.394134
Analysis finished2023-12-12 12:33:03.860227
Duration5.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
경상남도
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 31
100.0%

Length

2023-12-12T21:33:03.948458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:33:04.078537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 31
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
거창군
31 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 31
100.0%

Length

2023-12-12T21:33:04.202372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:33:04.310000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 31
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
48880
31 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 31
100.0%

Length

2023-12-12T21:33:04.437346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:33:04.556274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 31
100.0%

세목명
Categorical

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size380.0 B
자동차세
9 
지방소득세
9 
재산세
6 
등록면허세
3 
주민세
2 

Length

Max length5
Median length4
Mean length4.0645161
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 9
29.0%
지방소득세 9
29.0%
재산세 6
19.4%
등록면허세 3
 
9.7%
주민세 2
 
6.5%
취득세 2
 
6.5%

Length

2023-12-12T21:33:04.702574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:33:04.875117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 9
29.0%
지방소득세 9
29.0%
재산세 6
19.4%
등록면허세 3
 
9.7%
주민세 2
 
6.5%
취득세 2
 
6.5%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.1613
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:33:05.021883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12019
median2021
Q32021.5
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.6552296
Coefficient of variation (CV)0.00081935516
Kurtosis-0.78749743
Mean2020.1613
Median Absolute Deviation (MAD)1
Skewness-0.60422756
Sum62625
Variance2.7397849
MonotonicityIncreasing
2023-12-12T21:33:05.186996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 8
25.8%
2022 8
25.8%
2020 5
16.1%
2019 4
12.9%
2017 3
 
9.7%
2018 3
 
9.7%
ValueCountFrequency (%)
2017 3
 
9.7%
2018 3
 
9.7%
2019 4
12.9%
2020 5
16.1%
2021 8
25.8%
2022 8
25.8%
ValueCountFrequency (%)
2022 8
25.8%
2021 8
25.8%
2020 5
16.1%
2019 4
12.9%
2018 3
 
9.7%
2017 3
 
9.7%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
신규
31 

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 31
100.0%

Length

2023-12-12T21:33:05.369032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:33:05.522114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 31
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
개인
22 
법인
9 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 22
71.0%
법인 9
29.0%

Length

2023-12-12T21:33:05.701101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:33:05.872966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 22
71.0%
법인 9
29.0%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)64.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.741935
Minimum1
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:33:06.026432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q336.5
95-th percentile81.5
Maximum199
Range198
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation41.437477
Coefficient of variation (CV)1.5495317
Kurtosis9.319531
Mean26.741935
Median Absolute Deviation (MAD)6
Skewness2.7273359
Sum829
Variance1717.0645
MonotonicityNot monotonic
2023-12-12T21:33:06.211769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 6
19.4%
2 3
 
9.7%
3 3
 
9.7%
77 2
 
6.5%
4 2
 
6.5%
16 1
 
3.2%
86 1
 
3.2%
7 1
 
3.2%
43 1
 
3.2%
10 1
 
3.2%
Other values (10) 10
32.3%
ValueCountFrequency (%)
1 6
19.4%
2 3
9.7%
3 3
9.7%
4 2
 
6.5%
5 1
 
3.2%
7 1
 
3.2%
10 1
 
3.2%
13 1
 
3.2%
16 1
 
3.2%
19 1
 
3.2%
ValueCountFrequency (%)
199 1
3.2%
86 1
3.2%
77 2
6.5%
69 1
3.2%
57 1
3.2%
43 1
3.2%
42 1
3.2%
31 1
3.2%
26 1
3.2%
23 1
3.2%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean362577.42
Minimum210
Maximum2965420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:33:06.399887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile5105
Q129125
median85420
Q3457295
95-th percentile1115800
Maximum2965420
Range2965210
Interquartile range (IQR)428170

Descriptive statistics

Standard deviation592467.17
Coefficient of variation (CV)1.6340432
Kurtosis12.196479
Mean362577.42
Median Absolute Deviation (MAD)76420
Skewness3.126038
Sum11239900
Variance3.5101735 × 1011
MonotonicityNot monotonic
2023-12-12T21:33:06.572927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
112730 1
 
3.2%
30750 1
 
3.2%
47290 1
 
3.2%
25170 1
 
3.2%
620830 1
 
3.2%
27500 1
 
3.2%
45580 1
 
3.2%
382830 1
 
3.2%
85420 1
 
3.2%
888140 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
210 1
3.2%
1210 1
3.2%
9000 1
3.2%
11330 1
3.2%
21720 1
3.2%
25170 1
3.2%
26390 1
3.2%
27500 1
3.2%
30750 1
3.2%
38360 1
3.2%
ValueCountFrequency (%)
2965420 1
3.2%
1223720 1
3.2%
1007880 1
3.2%
888140 1
3.2%
780200 1
3.2%
780180 1
3.2%
620830 1
3.2%
531760 1
3.2%
382830 1
3.2%
365910 1
3.2%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.129032
Minimum1
Maximum303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:33:06.750159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16.5
median32
Q398.5
95-th percentile166.5
Maximum303
Range302
Interquartile range (IQR)92

Descriptive statistics

Standard deviation68.929308
Coefficient of variation (CV)1.2503268
Kurtosis4.365331
Mean55.129032
Median Absolute Deviation (MAD)30
Skewness1.888058
Sum1709
Variance4751.2495
MonotonicityNot monotonic
2023-12-12T21:33:06.958500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
7 3
 
9.7%
1 3
 
9.7%
2 3
 
9.7%
46 2
 
6.5%
32 2
 
6.5%
18 1
 
3.2%
10 1
 
3.2%
121 1
 
3.2%
61 1
 
3.2%
21 1
 
3.2%
Other values (13) 13
41.9%
ValueCountFrequency (%)
1 3
9.7%
2 3
9.7%
5 1
 
3.2%
6 1
 
3.2%
7 3
9.7%
9 1
 
3.2%
10 1
 
3.2%
18 1
 
3.2%
21 1
 
3.2%
32 2
6.5%
ValueCountFrequency (%)
303 1
3.2%
180 1
3.2%
153 1
3.2%
122 1
3.2%
121 1
3.2%
113 1
3.2%
104 1
3.2%
99 1
3.2%
98 1
3.2%
65 1
3.2%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean584060
Minimum9520
Maximum3995840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:33:07.163845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9520
5-th percentile14675
Q166700
median372710
Q3698370
95-th percentile1810825
Maximum3995840
Range3986320
Interquartile range (IQR)631670

Descriptive statistics

Standard deviation822875.14
Coefficient of variation (CV)1.408888
Kurtosis9.3053114
Mean584060
Median Absolute Deviation (MAD)311800
Skewness2.7302641
Sum18105860
Variance6.771235 × 1011
MonotonicityNot monotonic
2023-12-12T21:33:07.387567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
462650 1
 
3.2%
181970 1
 
3.2%
47290 1
 
3.2%
60910 1
 
3.2%
1171690 1
 
3.2%
27500 1
 
3.2%
97300 1
 
3.2%
503040 1
 
3.2%
191420 1
 
3.2%
1864340 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
9520 1
3.2%
11330 1
3.2%
18020 1
3.2%
27500 1
3.2%
47290 1
3.2%
47880 1
3.2%
55870 1
3.2%
60910 1
3.2%
72490 1
3.2%
97300 1
3.2%
ValueCountFrequency (%)
3995840 1
3.2%
1864340 1
3.2%
1757310 1
3.2%
1562270 1
3.2%
1171690 1
3.2%
1030420 1
3.2%
904980 1
3.2%
836070 1
3.2%
560670 1
3.2%
554390 1
3.2%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean771.40323
Minimum0
Maximum15038.84
Zeros4
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T21:33:07.599520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.37
median55.01
Q3161.995
95-th percentile2922.66
Maximum15038.84
Range15038.84
Interquartile range (IQR)143.625

Descriptive statistics

Standard deviation2770.6613
Coefficient of variation (CV)3.591716
Kurtosis25.437971
Mean771.40323
Median Absolute Deviation (MAD)54.91
Skewness4.9327821
Sum23913.5
Variance7676564
MonotonicityNot monotonic
2023-12-12T21:33:07.792352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 4
 
12.9%
310.41 1
 
3.2%
1412.32 1
 
3.2%
141.99 1
 
3.2%
88.73 1
 
3.2%
113.47 1
 
3.2%
31.4 1
 
3.2%
124.09 1
 
3.2%
109.92 1
 
3.2%
24.82 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
0.0 4
12.9%
2.0 1
 
3.2%
7.16 1
 
3.2%
8.47 1
 
3.2%
16.0 1
 
3.2%
20.74 1
 
3.2%
24.82 1
 
3.2%
31.4 1
 
3.2%
34.0 1
 
3.2%
34.75 1
 
3.2%
ValueCountFrequency (%)
15038.84 1
3.2%
4433.0 1
3.2%
1412.32 1
3.2%
504.31 1
3.2%
491.77 1
3.2%
456.68 1
3.2%
310.41 1
3.2%
182.0 1
3.2%
141.99 1
3.2%
124.09 1
3.2%

Interactions

2023-12-12T21:33:02.824962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:58.807688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.534442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:00.237828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:01.361665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:02.088474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:02.933245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:58.922502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.636185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:00.392462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:01.479078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:02.211687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:03.042962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.055199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.742484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:00.512235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:01.607053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:02.325901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:03.170805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.179116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.854822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:00.634618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:01.728964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:02.446924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:03.274633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.284815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.965853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:00.766366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:01.834537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:02.561045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:03.391006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:32:59.400810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:00.102617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:01.244390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:01.966219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:02.693785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:33:07.932478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.0000.0000.0000.0000.0000.000
과세년도0.0001.0000.1430.0000.3170.0000.0000.311
납세자유형0.0000.1431.0000.3830.0000.5110.0920.105
당해미환급건수0.0000.0000.3831.0000.9470.8220.9310.000
당해미환급금액0.0000.3170.0000.9471.0000.8340.9820.000
누적미환급건수0.0000.0000.5110.8220.8341.0000.8430.000
누적미환급금액0.0000.0000.0920.9310.9820.8431.0000.000
누적미환급금액증감0.0000.3110.1050.0000.0000.0000.0001.000
2023-12-12T21:33:08.058485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형
세목명1.0000.000
납세자유형0.0001.000
2023-12-12T21:33:08.177994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명납세자유형
과세년도1.0000.2470.248-0.0020.106-0.2700.0000.200
당해미환급건수0.2471.0000.8300.8240.8210.0030.0000.246
당해미환급금액0.2480.8301.0000.6250.876-0.3140.0000.000
누적미환급건수-0.0020.8240.6251.0000.8450.3710.0000.494
누적미환급금액0.1060.8210.8760.8451.0000.0920.0000.144
누적미환급금액증감-0.2700.003-0.3140.3710.0921.0000.0000.165
세목명0.0000.0000.0000.0000.0000.0001.0000.000
납세자유형0.2000.2460.0000.4940.1440.1650.0001.000

Missing values

2023-12-12T21:33:03.563480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:33:03.772051image/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
0경상남도거창군48880자동차세2017신규개인1611273046462650310.41
1경상남도거창군48880재산세2017신규개인33075098181970491.77
2경상남도거창군48880지방소득세2017신규개인28215079919020.74
3경상남도거창군48880자동차세2018신규개인199174065554390504.31
4경상남도거창군48880주민세2018신규개인1113301113300.0
5경상남도거창군48880지방소득세2018신규개인2217209120910456.68
6경상남도거창군48880등록면허세2019신규개인19000218020100.22
7경상남도거창군48880자동차세2019신규개인571007880122156227055.01
8경상남도거창군48880재산세2019신규개인112109918318015038.84
9경상남도거창군48880지방소득세2019신규개인232529303237384047.8
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
21경상남도거창군48880지방소득세2021신규법인53836074788024.82
22경상남도거창군48880취득세2021신규개인153176015317600.0
23경상남도거창군48880자동차세2022신규개인778881401801864340109.92
24경상남도거창군48880자동차세2022신규법인108542021191420124.09
25경상남도거창군48880재산세2022신규개인433828306150304031.4
26경상남도거창군48880재산세2022신규법인445580797300113.47
27경상남도거창군48880주민세2022신규법인1275001275000.0
28경상남도거창군48880지방소득세2022신규개인77620830121117169088.73
29경상남도거창군48880지방소득세2022신규법인7251701060910141.99
30경상남도거창군48880취득세2022신규개인2472902472900.0