Overview

Dataset statistics

Number of variables12
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory107.1 B

Variable types

Categorical6
Numeric6

Dataset

Description울산광역시 남구 지방세 미환급 현황에 대한 데이터로 시도명, 시군구명, 자치단체코드, 세목명, 과세년도, 미환급유형, 납세자유형 등의 항목을 제공합니다.
Author울산광역시 남구
URLhttps://www.data.go.kr/data/15078398/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 5 (15.6%) zerosZeros

Reproduction

Analysis started2024-04-29 22:42:19.320060
Analysis finished2024-04-29 22:42:24.384681
Duration5.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
울산광역시
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
울산광역시 32
100.0%

Length

2024-04-30T07:42:24.444865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:24.533655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산광역시 32
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
남구
32 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남구 32
100.0%

Length

2024-04-30T07:42:24.624799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:24.710138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남구 32
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
31140
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row31140
2nd row31140
3rd row31140
4th row31140
5th row31140

Common Values

ValueCountFrequency (%)
31140 32
100.0%

Length

2024-04-30T07:42:24.807830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:24.897739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31140 32
100.0%

세목명
Categorical

Distinct5
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
자동차세
12 
지방소득세
11 
재산세
4 
주민세
3 
등록면허세
2 

Length

Max length5
Median length4
Mean length4.1875
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자동차세 12
37.5%
지방소득세 11
34.4%
재산세 4
 
12.5%
주민세 3
 
9.4%
등록면허세 2
 
6.2%

Length

2024-04-30T07:42:25.040603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:25.186971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 12
37.5%
지방소득세 11
34.4%
재산세 4
 
12.5%
주민세 3
 
9.4%
등록면허세 2
 
6.2%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.7188
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-04-30T07:42:25.306400image/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 deviation1.7640565
Coefficient of variation (CV)0.00087341688
Kurtosis-1.3810656
Mean2019.7188
Median Absolute Deviation (MAD)2
Skewness-0.10588283
Sum64631
Variance3.1118952
MonotonicityDecreasing
2024-04-30T07:42:25.435780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 7
21.9%
2021 6
18.8%
2018 6
18.8%
2019 5
15.6%
2020 4
12.5%
2017 4
12.5%
ValueCountFrequency (%)
2017 4
12.5%
2018 6
18.8%
2019 5
15.6%
2020 4
12.5%
2021 6
18.8%
2022 7
21.9%
ValueCountFrequency (%)
2022 7
21.9%
2021 6
18.8%
2020 4
12.5%
2019 5
15.6%
2018 6
18.8%
2017 4
12.5%

미환급유형
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
신규
32 

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 32
100.0%

Length

2024-04-30T07:42:25.594059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:25.683950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 32
100.0%

납세자유형
Categorical

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
개인
20 
법인
12 

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 20
62.5%
법인 12
37.5%

Length

2024-04-30T07:42:25.774853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:25.866500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 20
62.5%
법인 12
37.5%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.34375
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-04-30T07:42:25.955117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median6
Q349
95-th percentile178.85
Maximum289
Range288
Interquartile range (IQR)46.25

Descriptive statistics

Standard deviation66.863177
Coefficient of variation (CV)1.6994612
Kurtosis6.3543046
Mean39.34375
Median Absolute Deviation (MAD)5
Skewness2.4705994
Sum1259
Variance4470.6845
MonotonicityNot monotonic
2024-04-30T07:42:26.058532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 6
18.8%
3 5
15.6%
2 2
 
6.2%
4 2
 
6.2%
58 2
 
6.2%
45 1
 
3.1%
33 1
 
3.1%
46 1
 
3.1%
10 1
 
3.1%
24 1
 
3.1%
Other values (10) 10
31.2%
ValueCountFrequency (%)
1 6
18.8%
2 2
 
6.2%
3 5
15.6%
4 2
 
6.2%
5 1
 
3.1%
7 1
 
3.1%
9 1
 
3.1%
10 1
 
3.1%
13 1
 
3.1%
24 1
 
3.1%
ValueCountFrequency (%)
289 1
3.1%
208 1
3.1%
155 1
3.1%
109 1
3.1%
84 1
3.1%
73 1
3.1%
58 2
6.2%
46 1
3.1%
45 1
3.1%
33 1
3.1%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446160.94
Minimum50
Maximum2316710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-04-30T07:42:26.171998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1886
Q114430
median78650
Q3600255
95-th percentile2115431
Maximum2316710
Range2316660
Interquartile range (IQR)585825

Descriptive statistics

Standard deviation683384.79
Coefficient of variation (CV)1.5317002
Kurtosis2.1416018
Mean446160.94
Median Absolute Deviation (MAD)76930
Skewness1.773164
Sum14277150
Variance4.6701478 × 1011
MonotonicityNot monotonic
2024-04-30T07:42:26.329376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
50 1
 
3.1%
614130 1
 
3.1%
694020 1
 
3.1%
6350 1
 
3.1%
27080 1
 
3.1%
226900 1
 
3.1%
175050 1
 
3.1%
595630 1
 
3.1%
5220 1
 
3.1%
60 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
50 1
3.1%
60 1
3.1%
3380 1
3.1%
5220 1
3.1%
6350 1
3.1%
9450 1
3.1%
11040 1
3.1%
12870 1
3.1%
14950 1
3.1%
27080 1
3.1%
ValueCountFrequency (%)
2316710 1
3.1%
2184720 1
3.1%
2058740 1
3.1%
1417640 1
3.1%
1380590 1
3.1%
825630 1
3.1%
694020 1
3.1%
614130 1
3.1%
595630 1
3.1%
480300 1
3.1%

누적미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.21875
Minimum1
Maximum498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-04-30T07:42:26.464873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14.75
median16.5
Q3130.5
95-th percentile322.45
Maximum498
Range497
Interquartile range (IQR)125.75

Descriptive statistics

Standard deviation123.76897
Coefficient of variation (CV)1.5238965
Kurtosis3.8796746
Mean81.21875
Median Absolute Deviation (MAD)14.5
Skewness2.000849
Sum2599
Variance15318.757
MonotonicityNot monotonic
2024-04-30T07:42:26.574008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 3
 
9.4%
5 3
 
9.4%
2 3
 
9.4%
14 2
 
6.2%
139 1
 
3.1%
82 1
 
3.1%
57 1
 
3.1%
4 1
 
3.1%
128 1
 
3.1%
15 1
 
3.1%
Other values (15) 15
46.9%
ValueCountFrequency (%)
1 3
9.4%
2 3
9.4%
3 1
 
3.1%
4 1
 
3.1%
5 3
9.4%
7 1
 
3.1%
8 1
 
3.1%
14 2
6.2%
15 1
 
3.1%
18 1
 
3.1%
ValueCountFrequency (%)
498 1
3.1%
400 1
3.1%
259 1
3.1%
237 1
3.1%
207 1
3.1%
194 1
3.1%
139 1
3.1%
138 1
3.1%
128 1
3.1%
82 1
3.1%

누적미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1071117.5
Minimum50
Maximum5683560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-04-30T07:42:26.704106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile6383
Q138117.5
median205720
Q31502755
95-th percentile5042267.5
Maximum5683560
Range5683510
Interquartile range (IQR)1464637.5

Descriptive statistics

Standard deviation1655472.5
Coefficient of variation (CV)1.5455564
Kurtosis2.5251126
Mean1071117.5
Median Absolute Deviation (MAD)196080
Skewness1.8501362
Sum34275760
Variance2.7405893 × 1012
MonotonicityNot monotonic
2024-04-30T07:42:26.842722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
50 1
 
3.1%
1458450 1
 
3.1%
1635670 1
 
3.1%
6350 1
 
3.1%
36700 1
 
3.1%
511180 1
 
3.1%
175260 1
 
3.1%
2231300 1
 
3.1%
14230 1
 
3.1%
6410 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
50 1
3.1%
6350 1
3.1%
6410 1
3.1%
12870 1
3.1%
14230 1
3.1%
14950 1
3.1%
36150 1
3.1%
36700 1
3.1%
38590 1
3.1%
42190 1
3.1%
ValueCountFrequency (%)
5683560 1
3.1%
5646360 1
3.1%
4548010 1
3.1%
3846770 1
3.1%
2466220 1
3.1%
2231300 1
3.1%
1713560 1
3.1%
1635670 1
3.1%
1458450 1
3.1%
1289800 1
3.1%

누적미환급금액증감
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean695.99813
Minimum0
Maximum10583.33
Zeros5
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-04-30T07:42:26.966230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131.565
median136.58
Q3200.705
95-th percentile3248.1705
Maximum10583.33
Range10583.33
Interquartile range (IQR)169.14

Descriptive statistics

Standard deviation2039.634
Coefficient of variation (CV)2.9305166
Kurtosis19.237782
Mean695.99813
Median Absolute Deviation (MAD)108.97
Skewness4.2676359
Sum22271.94
Variance4160107
MonotonicityNot monotonic
2024-04-30T07:42:27.089989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 5
 
15.6%
1405.5 1
 
3.1%
135.68 1
 
3.1%
35.52 1
 
3.1%
125.29 1
 
3.1%
0.12 1
 
3.1%
274.61 1
 
3.1%
172.61 1
 
3.1%
10583.33 1
 
3.1%
38.18 1
 
3.1%
Other values (18) 18
56.2%
ValueCountFrequency (%)
0.0 5
15.6%
0.12 1
 
3.1%
1.93 1
 
3.1%
19.7 1
 
3.1%
35.52 1
 
3.1%
38.18 1
 
3.1%
50.89 1
 
3.1%
78.64 1
 
3.1%
96.31 1
 
3.1%
107.55 1
 
3.1%
ValueCountFrequency (%)
10583.33 1
3.1%
5185.21 1
3.1%
1663.32 1
3.1%
1405.5 1
3.1%
458.97 1
3.1%
400.53 1
3.1%
331.33 1
3.1%
274.61 1
3.1%
176.07 1
3.1%
172.61 1
3.1%

Interactions

2024-04-30T07:42:23.461942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:20.952716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.586547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.073090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.513331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.971761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.541053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.208935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.665973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.143922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.589417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.070857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.621390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.286751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.743128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.220157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.663175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.158188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.697434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.361333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.817492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.291262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.734088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.235772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.782869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.436270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.901217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.367014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.804079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.310855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.858999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.511682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:21.986399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.439773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:22.882251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:23.386034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:42:27.176466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
세목명1.0000.0000.1850.0000.0000.0000.0000.089
과세년도0.0001.0000.0000.0000.0000.0000.0000.000
납세자유형0.1850.0001.0000.3690.3520.5630.4650.471
당해미환급건수0.0000.0000.3691.0000.9440.9590.9250.000
당해미환급금액0.0000.0000.3520.9441.0000.9880.9860.000
누적미환급건수0.0000.0000.5630.9590.9881.0000.9740.000
누적미환급금액0.0000.0000.4650.9250.9860.9741.0000.000
누적미환급금액증감0.0890.0000.4710.0000.0000.0000.0001.000
2024-04-30T07:42:27.294169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형
세목명1.0000.205
납세자유형0.2051.000
2024-04-30T07:42:27.393991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감세목명납세자유형
과세년도1.0000.1300.0840.0850.084-0.1290.0000.000
당해미환급건수0.1301.0000.9110.9500.9320.1770.0000.353
당해미환급금액0.0840.9111.0000.8680.9160.0070.0000.222
누적미환급건수0.0850.9500.8681.0000.9510.3630.0000.372
누적미환급금액0.0840.9320.9160.9511.0000.3310.0000.302
누적미환급금액증감-0.1290.1770.0070.3630.3311.0000.0250.303
세목명0.0000.0000.0000.0000.0000.0251.0000.205
납세자유형0.0000.3530.2220.3720.3020.3030.2051.000

Missing values

2024-04-30T07:42:23.977577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:42:24.141688image/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
0울산광역시남구31140등록면허세2022신규개인1501500.0
1울산광역시남구31140자동차세2022신규개인848256301941713560107.55
2울산광역시남구31140자동차세2022신규법인411040241946701663.32
3울산광역시남구31140재산세2022신규개인5361505361500.0
4울산광역시남구31140주민세2022신규개인1128701128700.0
5울산광역시남구31140지방소득세2022신규개인28921847204985646360158.45
6울산광역시남구31140지방소득세2022신규법인9449830144585201.93
7울산광역시남구31140등록면허세2021신규법인3149503149500.0
8울산광역시남구31140자동차세2021신규개인1551380590259246622078.64
9울산광역시남구31140자동차세2021신규법인1310717029258700141.39
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액누적미환급건수누적미환급금액누적미환급금액증감
22울산광역시남구31140자동차세2018신규개인2433314081844320153.44
23울산광역시남구31140자동차세2018신규법인10961201513282038.18
24울산광역시남구31140재산세2018신규개인1602641010583.33
25울산광역시남구31140주민세2018신규개인15220214230172.61
26울산광역시남구31140지방소득세2018신규개인465956301282231300274.61
27울산광역시남구31140지방소득세2018신규법인217505041752600.12
28울산광역시남구31140자동차세2017신규개인3322690057511180125.29
29울산광역시남구31140자동차세2017신규법인32708053670035.52
30울산광역시남구31140재산세2017신규개인16350163500.0
31울산광역시남구31140지방소득세2017신규개인45694020821635670135.68