Overview

Dataset statistics

Number of variables12
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory106.8 B

Variable types

Categorical6
Numeric6

Dataset

Description본 데이터는 경상남도 합천군의 년도별 지방세 미환급현황으로 세목명, 미환급유형, 납세자유형, 당해미환급건수, 당해미환급금액, 누적미환급건수, 누적미환급금액, 누적미환급금액증감 등의 정보를 제공하고 있습니다.
Author경상남도 합천군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15089296

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
누적미환급금액 has unique valuesUnique
누적미환급금액증감 has 5 (14.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:44:56.013478
Analysis finished2023-12-11 00:44:59.984871
Duration3.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
경상남도
35 

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 35
100.0%

Length

2023-12-11T09:45:00.075849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:45:00.159724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 35
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
합천군
35 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
합천군 35
100.0%

Length

2023-12-11T09:45:00.253168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:45:00.357038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
합천군 35
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
48890
35 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48890
2nd row48890
3rd row48890
4th row48890
5th row48890

Common Values

ValueCountFrequency (%)
48890 35
100.0%

Length

2023-12-11T09:45:00.446271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:45:00.527176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48890 35
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.7143
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:45:00.604104image/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 deviation1.7417271
Coefficient of variation (CV)0.00086236314
Kurtosis-1.1984415
Mean2019.7143
Median Absolute Deviation (MAD)1
Skewness-0.31157835
Sum70690
Variance3.0336134
MonotonicityNot monotonic
2023-12-11T09:45:00.696169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 8
22.9%
2020 7
20.0%
2017 6
17.1%
2022 6
17.1%
2018 4
11.4%
2019 4
11.4%
ValueCountFrequency (%)
2017 6
17.1%
2018 4
11.4%
2019 4
11.4%
2020 7
20.0%
2021 8
22.9%
2022 6
17.1%
ValueCountFrequency (%)
2022 6
17.1%
2021 8
22.9%
2020 7
20.0%
2019 4
11.4%
2018 4
11.4%
2017 6
17.1%

세목명
Categorical

Distinct6
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
자동차세
12 
지방소득세
10 
재산세
9 
등록면허세
2 
주민세
 
1

Length

Max length5
Median length4
Mean length4.0285714
Min length3

Unique

Unique2 ?
Unique (%)5.7%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 12
34.3%
지방소득세 10
28.6%
재산세 9
25.7%
등록면허세 2
 
5.7%
주민세 1
 
2.9%
취득세 1
 
2.9%

Length

2023-12-11T09:45:00.812872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:45:00.950724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 12
34.3%
지방소득세 10
28.6%
재산세 9
25.7%
등록면허세 2
 
5.7%
주민세 1
 
2.9%
취득세 1
 
2.9%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
신규
35 

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 35
100.0%

Length

2023-12-11T09:45:01.107321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:45:01.189516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 35
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
개인
21 
법인
14 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 21
60.0%
법인 14
40.0%

Length

2023-12-11T09:45:01.273598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:45:01.360322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 21
60.0%
법인 14
40.0%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.342857
Minimum1
Maximum172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:45:01.681551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q320
95-th percentile61.3
Maximum172
Range171
Interquartile range (IQR)17

Descriptive statistics

Standard deviation31.430542
Coefficient of variation (CV)1.7135031
Kurtosis17.147481
Mean18.342857
Median Absolute Deviation (MAD)6
Skewness3.798299
Sum642
Variance987.87899
MonotonicityNot monotonic
2023-12-11T09:45:01.778867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 5
14.3%
7 4
 
11.4%
2 3
 
8.6%
4 3
 
8.6%
15 2
 
5.7%
21 2
 
5.7%
3 2
 
5.7%
41 1
 
2.9%
19 1
 
2.9%
5 1
 
2.9%
Other values (11) 11
31.4%
ValueCountFrequency (%)
1 5
14.3%
2 3
8.6%
3 2
 
5.7%
4 3
8.6%
5 1
 
2.9%
6 1
 
2.9%
7 4
11.4%
8 1
 
2.9%
10 1
 
2.9%
12 1
 
2.9%
ValueCountFrequency (%)
172 1
2.9%
69 1
2.9%
58 1
2.9%
42 1
2.9%
41 1
2.9%
32 1
2.9%
26 1
2.9%
21 2
5.7%
19 1
2.9%
15 2
5.7%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean788167.71
Minimum590
Maximum19330740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:45:01.888921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum590
5-th percentile2595
Q118815
median76760
Q3313890
95-th percentile1409923
Maximum19330740
Range19330150
Interquartile range (IQR)295075

Descriptive statistics

Standard deviation3260720.1
Coefficient of variation (CV)4.1370891
Kurtosis33.420501
Mean788167.71
Median Absolute Deviation (MAD)69960
Skewness5.7324273
Sum27585870
Variance1.0632295 × 1013
MonotonicityNot monotonic
2023-12-11T09:45:02.016038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5340 2
 
5.7%
135100 1
 
2.9%
30110 1
 
2.9%
177510 1
 
2.9%
146720 1
 
2.9%
46650 1
 
2.9%
430530 1
 
2.9%
43640 1
 
2.9%
357230 1
 
2.9%
76760 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
590 1
2.9%
810 1
2.9%
3360 1
2.9%
5340 2
5.7%
8640 1
2.9%
9400 1
2.9%
12570 1
2.9%
16880 1
2.9%
20750 1
2.9%
22970 1
2.9%
ValueCountFrequency (%)
19330740 1
2.9%
2592860 1
2.9%
902950 1
2.9%
827920 1
2.9%
732230 1
2.9%
430530 1
2.9%
357230 1
2.9%
334350 1
2.9%
330650 1
2.9%
297130 1
2.9%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.742857
Minimum1
Maximum342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:45:02.164647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q16.5
median16
Q350
95-th percentile181.1
Maximum342
Range341
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation71.191681
Coefficient of variation (CV)1.5563453
Kurtosis8.4120043
Mean45.742857
Median Absolute Deviation (MAD)12
Skewness2.7079952
Sum1601
Variance5068.2555
MonotonicityNot monotonic
2023-12-11T09:45:02.277608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2 4
 
11.4%
15 2
 
5.7%
1 2
 
5.7%
10 2
 
5.7%
13 2
 
5.7%
28 2
 
5.7%
52 1
 
2.9%
20 1
 
2.9%
7 1
 
2.9%
57 1
 
2.9%
Other values (17) 17
48.6%
ValueCountFrequency (%)
1 2
5.7%
2 4
11.4%
3 1
 
2.9%
4 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
10 2
5.7%
12 1
 
2.9%
13 2
5.7%
15 2
5.7%
ValueCountFrequency (%)
342 1
2.9%
186 1
2.9%
179 1
2.9%
142 1
2.9%
108 1
2.9%
101 1
2.9%
75 1
2.9%
57 1
2.9%
52 1
2.9%
48 1
2.9%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1064389.1
Minimum1730
Maximum19950060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:45:02.477027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile5103
Q149325
median179810
Q3674865
95-th percentile3035886
Maximum19950060
Range19948330
Interquartile range (IQR)625540

Descriptive statistics

Standard deviation3398331.2
Coefficient of variation (CV)3.1927526
Kurtosis30.225594
Mean1064389.1
Median Absolute Deviation (MAD)156840
Skewness5.3671816
Sum37253620
Variance1.1548655 × 1013
MonotonicityNot monotonic
2023-12-11T09:45:02.623438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
135100 1
 
2.9%
1853460 1
 
2.9%
5340 1
 
2.9%
889820 1
 
2.9%
179810 1
 
2.9%
112210 1
 
2.9%
134670 1
 
2.9%
1320350 1
 
2.9%
223450 1
 
2.9%
142320 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1730 1
2.9%
4550 1
2.9%
5340 1
2.9%
8640 1
2.9%
15100 1
2.9%
22970 1
2.9%
25300 1
2.9%
28720 1
2.9%
33090 1
2.9%
65560 1
2.9%
ValueCountFrequency (%)
19950060 1
2.9%
4332020 1
2.9%
2480400 1
2.9%
1853460 1
2.9%
1320350 1
2.9%
970280 1
2.9%
889820 1
2.9%
829190 1
2.9%
712310 1
2.9%
637420 1
2.9%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.63943
Minimum0
Maximum2421.91
Zeros5
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T09:45:02.765827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.695
median113.04
Q3238.245
95-th percentile929.361
Maximum2421.91
Range2421.91
Interquartile range (IQR)223.55

Descriptive statistics

Standard deviation452.47193
Coefficient of variation (CV)1.8052703
Kurtosis15.843464
Mean250.63943
Median Absolute Deviation (MAD)109.84
Skewness3.6522266
Sum8772.38
Variance204730.85
MonotonicityNot monotonic
2023-12-11T09:45:02.899673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 5
 
14.3%
123.87 1
 
2.9%
60.64 1
 
2.9%
237.74 1
 
2.9%
1.14 1
 
2.9%
94.08 1
 
2.9%
7.46 1
 
2.9%
238.75 1
 
2.9%
37.7 1
 
2.9%
372.67 1
 
2.9%
Other values (21) 21
60.0%
ValueCountFrequency (%)
0.0 5
14.3%
1.14 1
 
2.9%
1.2 1
 
2.9%
3.2 1
 
2.9%
7.46 1
 
2.9%
21.93 1
 
2.9%
22.55 1
 
2.9%
37.7 1
 
2.9%
52.0 1
 
2.9%
60.64 1
 
2.9%
ValueCountFrequency (%)
2421.91 1
2.9%
1033.29 1
2.9%
884.82 1
2.9%
671.19 1
2.9%
421.56 1
2.9%
412.03 1
2.9%
401.28 1
2.9%
372.67 1
2.9%
238.75 1
2.9%
237.74 1
2.9%

Interactions

2023-12-11T09:44:59.262206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:56.287483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:56.843111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.409591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.032999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.633489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:59.338156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:56.366036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:56.933883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.502994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.150988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.746248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:59.419652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:56.466207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.026608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.594711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.264390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.850717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:59.503167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:56.554849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.130583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.683301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.369521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.982919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:59.592868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:56.654218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.214447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.827257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.456678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:59.081078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:59.678915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:56.754888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.314509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:57.930647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:58.548537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:44:59.171534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:45:03.011251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
과세년도1.0000.0000.0000.0000.0000.0000.0000.615
세목명0.0001.0000.0000.0000.0000.0000.0000.000
납세자유형0.0000.0001.0000.3400.0000.3170.0000.203
당해미환급건수0.0000.0000.3401.0000.8150.9410.8160.000
당해미환급금액0.0000.0000.0000.8151.0001.0001.0000.000
누적미환급건수0.0000.0000.3170.9411.0001.0000.9180.000
누적미환급금액0.0000.0000.0000.8161.0000.9181.0000.000
누적미환급금액증감0.6150.0000.2030.0000.0000.0000.0001.000
2023-12-11T09:45:03.120961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납세자유형세목명
납세자유형1.0000.000
세목명0.0001.000
2023-12-11T09:45:03.234389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명납세자유형
과세년도1.0000.2600.2330.1660.171-0.2460.0000.000
당해미환급건수0.2601.0000.8680.9270.8760.0620.0000.392
당해미환급금액0.2330.8681.0000.7890.907-0.1720.0000.000
누적미환급건수0.1660.9270.7891.0000.9210.3410.0000.305
누적미환급금액0.1710.8760.9070.9211.0000.1970.0000.000
누적미환급금액증감-0.2460.062-0.1720.3410.1971.0000.0000.121
세목명0.0000.0000.0000.0000.0000.0001.0000.000
납세자유형0.0000.3920.0000.3050.0000.1210.0001.000

Missing values

2023-12-11T09:44:59.784675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:44:59.926064image/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

시도명시군구명자치단체코드과세년도세목명미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
0경상남도합천군488902020등록면허세신규개인15135100151351000.0
1경상남도합천군488902020자동차세신규개인698279201791853460123.87
2경상남도합천군488902020자동차세신규법인216880151913001033.29
3경상남도합천군488902020재산세신규개인75922028184920212.26
4경상남도합천군488902020주민세신규개인2287202287200.0
5경상남도합천군488902020지방소득세신규개인3229713052637420114.53
6경상남도합천군488902020지방소득세신규법인159024550671.19
7경상남도합천군488902021등록면허세신규법인18640186400.0
8경상남도합천군488902021자동차세신규개인1722592860342433202067.07
9경상남도합천군488902021자동차세신규법인123306502550258052.0
시도명시군구명자치단체코드과세년도세목명미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
25경상남도합천군488902019자동차세신규개인414305301421320350206.68
26경상남도합천군488902019자동차세신규법인64364016223450412.03
27경상남도합천군488902019재산세신규개인73011027142320372.67
28경상남도합천군488902019지방소득세신규개인133572302649190037.7
29경상남도합천군488902022자동차세신규개인427322301862480400238.75
30경상남도합천군488902022자동차세신규법인15902950239702807.46
31경상남도합천군488902022재산세신규개인81158102822476094.08
32경상남도합천군488902022재산세신규법인5808006817201.14
33경상남도합천군488902022지방소득세신규개인1924551057829190237.74
34경상남도합천군488902022지방소득세신규법인4940071510060.64