Overview

Dataset statistics

Number of variables12
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory107.7 B

Variable types

Categorical7
Numeric5

Dataset

Description2017년부터 2019년 지방세 세목별 미환급 건수와 지방세 세목별 미환급 유형, 지방세 미환급 금액, 지방세 미환급 건수 데이터입니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079427

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 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 is highly overall correlated with 당해미환급금액 and 1 other fieldsHigh correlation
과세년도 is highly overall correlated with 누적미환급건수High correlation
미환급유형 is highly overall correlated with 누적미환급금액증감 and 1 other fieldsHigh correlation
납세자유형 is highly overall correlated with 미환급유형High correlation
당해미환급금액 has unique valuesUnique
누적미환급금액증감 has unique valuesUnique
누적미환급금액증감 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2023-12-11 00:38:14.163494
Analysis finished2023-12-11 00:38:17.557618
Duration3.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
경상남도
28 

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 28
100.0%

Length

2023-12-11T09:38:17.636465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:17.747754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 28
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
양산시
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양산시 28
100.0%

Length

2023-12-11T09:38:17.842635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:17.934606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양산시 28
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
48330
28 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48330
2nd row48330
3rd row48330
4th row48330
5th row48330

Common Values

ValueCountFrequency (%)
48330 28
100.0%

Length

2023-12-11T09:38:18.029151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:18.132705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48330 28
100.0%

세목명
Categorical

Distinct5
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size356.0 B
자동차세
12 
지방소득세
8 
재산세
4 
주민세
3 
등록면허세
 
1

Length

Max length5
Median length4
Mean length4.0714286
Min length3

Unique

Unique1 ?
Unique (%)3.6%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 12
42.9%
지방소득세 8
28.6%
재산세 4
 
14.3%
주민세 3
 
10.7%
등록면허세 1
 
3.6%

Length

2023-12-11T09:38:18.238627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:18.372840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 12
42.9%
지방소득세 8
28.6%
재산세 4
 
14.3%
주민세 3
 
10.7%
등록면허세 1
 
3.6%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size356.0 B
2017
10 
2018
9 
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 (%)
2017 10
35.7%
2018 9
32.1%
2019 9
32.1%

Length

2023-12-11T09:38:18.509069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:18.610981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 10
35.7%
2018 9
32.1%
2019 9
32.1%

미환급유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size356.0 B
신규
19 
폐업 또는 부도
4 
주소불명
3 
사망
2 

Length

Max length8
Median length2
Mean length3.0714286
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신규 19
67.9%
폐업 또는 부도 4
 
14.3%
주소불명 3
 
10.7%
사망 2
 
7.1%

Length

2023-12-11T09:38:18.722417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:18.821563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 19
52.8%
폐업 4
 
11.1%
또는 4
 
11.1%
부도 4
 
11.1%
주소불명 3
 
8.3%
사망 2
 
5.6%

납세자유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
개인
14 
법인
14 

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 (%)
개인 14
50.0%
법인 14
50.0%

Length

2023-12-11T09:38:18.928430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:38:19.044408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 14
50.0%
법인 14
50.0%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56
Minimum1
Maximum369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T09:38:19.154292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q379.25
95-th percentile191.7
Maximum369
Range368
Interquartile range (IQR)77.25

Descriptive statistics

Standard deviation88.157687
Coefficient of variation (CV)1.5742444
Kurtosis4.7658961
Mean56
Median Absolute Deviation (MAD)9
Skewness2.0749456
Sum1568
Variance7771.7778
MonotonicityNot monotonic
2023-12-11T09:38:19.288645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 6
21.4%
2 4
14.3%
5 2
 
7.1%
10 2
 
7.1%
13 2
 
7.1%
62 1
 
3.6%
198 1
 
3.6%
38 1
 
3.6%
156 1
 
3.6%
369 1
 
3.6%
Other values (7) 7
25.0%
ValueCountFrequency (%)
1 6
21.4%
2 4
14.3%
4 1
 
3.6%
5 2
 
7.1%
10 2
 
7.1%
13 2
 
7.1%
22 1
 
3.6%
38 1
 
3.6%
62 1
 
3.6%
71 1
 
3.6%
ValueCountFrequency (%)
369 1
3.6%
198 1
3.6%
180 1
3.6%
156 1
3.6%
153 1
3.6%
141 1
3.6%
104 1
3.6%
71 1
3.6%
62 1
3.6%
38 1
3.6%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean876755.36
Minimum20
Maximum6141630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T09:38:19.415485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile3580.5
Q130532.5
median186420
Q31246252.5
95-th percentile3661574.5
Maximum6141630
Range6141610
Interquartile range (IQR)1215720

Descriptive statistics

Standard deviation1443727.7
Coefficient of variation (CV)1.6466711
Kurtosis6.6522978
Mean876755.36
Median Absolute Deviation (MAD)180235
Skewness2.4830668
Sum24549150
Variance2.0843496 × 1012
MonotonicityNot monotonic
2023-12-11T09:38:19.853882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
31800 1
 
3.6%
20 1
 
3.6%
148930 1
 
3.6%
4419440 1
 
3.6%
54890 1
 
3.6%
54880 1
 
3.6%
26730 1
 
3.6%
309990 1
 
3.6%
1439780 1
 
3.6%
6141630 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
20 1
3.6%
2240 1
3.6%
6070 1
3.6%
6300 1
3.6%
15800 1
3.6%
25840 1
3.6%
26730 1
3.6%
31800 1
3.6%
35570 1
3.6%
54880 1
3.6%
ValueCountFrequency (%)
6141630 1
3.6%
4419440 1
3.6%
2254110 1
3.6%
2202920 1
3.6%
1724030 1
3.6%
1439780 1
3.6%
1346820 1
3.6%
1212730 1
3.6%
915550 1
3.6%
875550 1
3.6%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean407.07143
Minimum1
Maximum1206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T09:38:19.982483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q119.75
median282
Q3689.75
95-th percentile1129.7
Maximum1206
Range1205
Interquartile range (IQR)670

Descriptive statistics

Standard deviation417.16023
Coefficient of variation (CV)1.0247839
Kurtosis-1.1821829
Mean407.07143
Median Absolute Deviation (MAD)279.5
Skewness0.50676007
Sum11398
Variance174022.66
MonotonicityNot monotonic
2023-12-11T09:38:20.095443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
640 3
 
10.7%
1206 2
 
7.1%
29 2
 
7.1%
794 2
 
7.1%
655 2
 
7.1%
988 2
 
7.1%
46 2
 
7.1%
6 1
 
3.6%
56 1
 
3.6%
511 1
 
3.6%
Other values (10) 10
35.7%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
6 1
3.6%
8 1
3.6%
17 1
3.6%
19 1
3.6%
20 1
3.6%
29 2
7.1%
46 2
7.1%
ValueCountFrequency (%)
1206 2
7.1%
988 2
7.1%
835 1
 
3.6%
794 2
7.1%
655 2
7.1%
640 3
10.7%
511 1
 
3.6%
313 1
 
3.6%
251 1
 
3.6%
56 1
 
3.6%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5618129.3
Minimum9230
Maximum15209830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T09:38:20.205500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9230
5-th percentile58117
Q1369857.5
median6167755
Q38552030
95-th percentile13492019
Maximum15209830
Range15200600
Interquartile range (IQR)8182172.5

Descriptive statistics

Standard deviation4420434.5
Coefficient of variation (CV)0.78681609
Kurtosis-0.29464396
Mean5618129.3
Median Absolute Deviation (MAD)2647495
Skewness0.37492599
Sum1.5730762 × 108
Variance1.9540242 × 1013
MonotonicityNot monotonic
2023-12-11T09:38:20.328076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
7275610 3
 
10.7%
15209830 2
 
7.1%
3813340 2
 
7.1%
8552030 2
 
7.1%
6859210 2
 
7.1%
10301800 2
 
7.1%
6093290 2
 
7.1%
73750 1
 
3.6%
6242220 1
 
3.6%
8902660 1
 
3.6%
Other values (10) 10
35.7%
ValueCountFrequency (%)
9230 1
3.6%
54890 1
3.6%
64110 1
3.6%
73750 1
3.6%
281320 1
3.6%
314240 1
3.6%
349810 1
3.6%
376540 1
3.6%
3607670 1
3.6%
3813340 2
7.1%
ValueCountFrequency (%)
15209830 2
7.1%
10301800 2
7.1%
9062130 1
 
3.6%
8902660 1
 
3.6%
8552030 2
7.1%
7275610 3
10.7%
6859210 2
7.1%
6242220 1
 
3.6%
6093290 2
7.1%
4483220 1
 
3.6%

누적미환급금액증감
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17997.254
Minimum0
Maximum250473.81
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T09:38:20.453422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.8985
Q1265.975
median610.35
Q38704.89
95-th percentile86822.127
Maximum250473.81
Range250473.81
Interquartile range (IQR)8438.915

Descriptive statistics

Standard deviation50630.836
Coefficient of variation (CV)2.8132534
Kurtosis17.695848
Mean17997.254
Median Absolute Deviation (MAD)598.085
Skewness4.065988
Sum503923.12
Variance2.5634816 × 109
MonotonicityNot monotonic
2023-12-11T09:38:20.577303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
131.92 1
 
3.6%
46050.0 1
 
3.6%
4091.38 1
 
3.6%
101.44 1
 
3.6%
0.0 1
 
3.6%
16.82 1
 
3.6%
1308.68 1
 
3.6%
3223.27 1
 
3.6%
615.51 1
 
3.6%
147.65 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0.0 1
3.6%
7.71 1
3.6%
16.82 1
3.6%
101.44 1
3.6%
131.92 1
3.6%
147.65 1
3.6%
170.32 1
3.6%
297.86 1
3.6%
311.37 1
3.6%
316.51 1
3.6%
ValueCountFrequency (%)
250473.81 1
3.6%
108776.35 1
3.6%
46050.0 1
3.6%
24035.06 1
3.6%
23480.84 1
3.6%
13928.57 1
3.6%
13327.59 1
3.6%
7163.99 1
3.6%
4091.38 1
3.6%
3223.27 1
3.6%

Interactions

2023-12-11T09:38:16.670295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:14.697139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.189502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.641414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:16.129118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:16.792986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:14.823556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.274111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.742289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:16.249609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:16.895047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:14.935044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.365998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.853964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:16.353645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:17.000466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.020829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.466707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.932812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:16.460907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:17.130682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.106593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:15.557405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:16.029819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:38:16.569476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:38:20.678042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.0000.0000.0000.0000.4940.6080.000
과세년도0.0001.0000.0000.0000.0000.4690.6770.7500.023
미환급유형0.0000.0001.0000.7490.0000.0000.0460.0000.853
납세자유형0.0000.0000.7491.0000.0000.0000.2530.4440.181
당해미환급건수0.0000.0000.0000.0001.0000.8880.9350.5740.000
당해미환급금액0.0000.4690.0000.0000.8881.0000.7720.8040.000
누적미환급건수0.4940.6770.0460.2530.9350.7721.0000.9240.200
누적미환급금액0.6080.7500.0000.4440.5740.8040.9241.0000.661
누적미환급금액증감0.0000.0230.8530.1810.0000.0000.2000.6611.000
2023-12-11T09:38:20.824031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납세자유형미환급유형세목명과세년도
납세자유형1.0000.5170.0000.000
미환급유형0.5171.0000.0000.000
세목명0.0000.0001.0000.000
과세년도0.0000.0000.0001.000
2023-12-11T09:38:20.940992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명과세년도미환급유형납세자유형
당해미환급건수1.0000.7080.6720.678-0.1960.0000.0000.0000.000
당해미환급금액0.7081.0000.4480.503-0.6280.0000.1890.0000.000
누적미환급건수0.6720.4481.0000.9580.2410.3170.5320.0000.227
누적미환급금액0.6780.5030.9581.0000.1820.4570.3930.0000.323
누적미환급금액증감-0.196-0.6280.2410.1821.0000.0000.0000.5050.096
세목명0.0000.0000.3170.4570.0001.0000.0000.0000.000
과세년도0.0000.1890.5320.3930.0000.0001.0000.0000.000
미환급유형0.0000.0000.0000.0000.5050.0000.0001.0000.517
납세자유형0.0000.0000.2270.3230.0960.0000.0000.5171.000

Missing values

2023-12-11T09:38:17.281393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:38:17.477106image/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
0경상남도양산시48330등록면허세2017신규개인231800673750131.92
1경상남도양산시48330자동차세2017사망개인163006556859210108776.35
2경상남도양산시48330자동차세2017신규개인15317240306556859210297.86
3경상남도양산시48330자동차세2017신규법인10413468206407275610440.21
4경상남도양산시48330자동차세2017주소불명법인2222391064072756103149.35
5경상남도양산시48330자동차세2017폐업 또는 부도법인510016064072756107163.99
6경상남도양산시48330재산세2017신규개인222401731424013928.57
7경상남도양산시48330지방소득세2017신규개인716485902513607670456.23
8경상남도양산시48330지방소득세2017신규법인101580029381334024035.06
9경상남도양산시48330지방소득세2017폐업 또는 부도법인1915550293813340316.51
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
18경상남도양산시48330지방소득세2018폐업 또는 부도법인42254110466093290170.32
19경상남도양산시48330자동차세2019사망개인26070120615209830250473.81
20경상남도양산시48330자동차세2019신규개인3696141630120615209830147.65
21경상남도양산시48330자동차세2019신규법인156143978098810301800615.51
22경상남도양산시48330자동차세2019주소불명법인38309990988103018003223.27
23경상남도양산시48330재산세2019신규개인126730203765401308.68
24경상남도양산시48330주민세2019신규개인55488086411016.82
25경상남도양산시48330주민세2019신규법인1548901548900.0
26경상남도양산시48330지방소득세2019신규개인19844194405118902660101.44
27경상남도양산시48330지방소득세2019신규법인101489305662422204091.38