Overview

Dataset statistics

Number of variables11
Number of observations101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory96.3 B

Variable types

Numeric5
Categorical6

Dataset

Description지방세 체납현황의 데이터로 과세년도, 세목명, 제납액구간, 체납건수, 체납금액, 누적체납건수, 누적체납금액등의 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15078672/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
연번 is highly overall correlated with 과세년도High correlation
체납건수 is highly overall correlated with 누적체납건수High correlation
체납금액 is highly overall correlated with 누적체납금액 and 1 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
과세년도 is highly overall correlated with 연번High correlation
체납액구간 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
연번 has unique valuesUnique
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:09:15.255037
Analysis finished2023-12-12 03:09:18.566160
Duration3.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T12:09:18.652219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median51
Q376
95-th percentile96
Maximum101
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.300171
Coefficient of variation (CV)0.57451315
Kurtosis-1.2
Mean51
Median Absolute Deviation (MAD)25
Skewness0
Sum5151
Variance858.5
MonotonicityStrictly increasing
2023-12-12T12:09:18.847482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
전라남도
101 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 101
100.0%

Length

2023-12-12T12:09:19.035219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:19.161960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 101
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
장흥군
101 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
장흥군 101
100.0%

Length

2023-12-12T12:09:19.296938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:19.422164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장흥군 101
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
46800
101 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46800
2nd row46800
3rd row46800
4th row46800
5th row46800

Common Values

ValueCountFrequency (%)
46800 101
100.0%

Length

2023-12-12T12:09:19.545624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:19.663055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46800 101
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2020
32 
2021
26 
2019
25 
2018
12 
2017
6 

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 (%)
2020 32
31.7%
2021 26
25.7%
2019 25
24.8%
2018 12
 
11.9%
2017 6
 
5.9%

Length

2023-12-12T12:09:19.829903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:19.976530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 32
31.7%
2021 26
25.7%
2019 25
24.8%
2018 12
 
11.9%
2017 6
 
5.9%

세목명
Categorical

Distinct7
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
재산세
23 
지방소득세
23 
취득세
22 
자동차세
13 
주민세
13 
Other values (2)
7 

Length

Max length7
Median length3
Mean length3.7623762
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 23
22.8%
지방소득세 23
22.8%
취득세 22
21.8%
자동차세 13
12.9%
주민세 13
12.9%
등록면허세 5
 
5.0%
지역자원시설세 2
 
2.0%

Length

2023-12-12T12:09:20.177535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:20.332423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 23
22.8%
지방소득세 23
22.8%
취득세 22
21.8%
자동차세 13
12.9%
주민세 13
12.9%
등록면허세 5
 
5.0%
지역자원시설세 2
 
2.0%

체납액구간
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
10만원 미만
28 
10만원~30만원미만
19 
30만원~50만원미만
14 
1백만원~3백만원미만
10 
50만원~1백만원미만
10 
Other values (5)
20 

Length

Max length11
Median length11
Mean length9.8811881
Min length7

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row10만원 미만
5th row10만원 미만

Common Values

ValueCountFrequency (%)
10만원 미만 28
27.7%
10만원~30만원미만 19
18.8%
30만원~50만원미만 14
13.9%
1백만원~3백만원미만 10
 
9.9%
50만원~1백만원미만 10
 
9.9%
3백만원~5백만원미만 7
 
6.9%
1천만원~3천만원미만 6
 
5.9%
5백만원~1천만원미만 5
 
5.0%
3천만원~5천만원미만 1
 
1.0%
5천만원~1억원미만 1
 
1.0%

Length

2023-12-12T12:09:20.558973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:09:20.756014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 28
21.7%
미만 28
21.7%
10만원~30만원미만 19
14.7%
30만원~50만원미만 14
10.9%
1백만원~3백만원미만 10
 
7.8%
50만원~1백만원미만 10
 
7.8%
3백만원~5백만원미만 7
 
5.4%
1천만원~3천만원미만 6
 
4.7%
5백만원~1천만원미만 5
 
3.9%
3천만원~5천만원미만 1
 
0.8%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.059406
Minimum1
Maximum1219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T12:09:20.975977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q312
95-th percentile344
Maximum1219
Range1218
Interquartile range (IQR)11

Descriptive statistics

Standard deviation163.22425
Coefficient of variation (CV)3.0762548
Kurtosis29.425359
Mean53.059406
Median Absolute Deviation (MAD)2
Skewness5.035713
Sum5359
Variance26642.156
MonotonicityNot monotonic
2023-12-12T12:09:21.157439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 30
29.7%
2 11
 
10.9%
3 11
 
10.9%
6 4
 
4.0%
5 4
 
4.0%
12 4
 
4.0%
4 4
 
4.0%
10 2
 
2.0%
344 2
 
2.0%
7 2
 
2.0%
Other values (24) 27
26.7%
ValueCountFrequency (%)
1 30
29.7%
2 11
 
10.9%
3 11
 
10.9%
4 4
 
4.0%
5 4
 
4.0%
6 4
 
4.0%
7 2
 
2.0%
8 1
 
1.0%
9 1
 
1.0%
10 2
 
2.0%
ValueCountFrequency (%)
1219 1
1.0%
689 1
1.0%
661 1
1.0%
363 1
1.0%
344 2
2.0%
212 2
2.0%
164 1
1.0%
135 1
1.0%
100 1
1.0%
80 1
1.0%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6818351.5
Minimum3040
Maximum1.1477667 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T12:09:21.635262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3040
5-th percentile117380
Q1645270
median2423980
Q38092420
95-th percentile19536560
Maximum1.1477667 × 108
Range1.1477363 × 108
Interquartile range (IQR)7447150

Descriptive statistics

Standard deviation14238272
Coefficient of variation (CV)2.088228
Kurtosis35.483532
Mean6818351.5
Median Absolute Deviation (MAD)2079670
Skewness5.385484
Sum6.886535 × 108
Variance2.027284 × 1014
MonotonicityNot monotonic
2023-12-12T12:09:21.804516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6550 1
 
1.0%
18142310 1
 
1.0%
57782110 1
 
1.0%
8092420 1
 
1.0%
6032820 1
 
1.0%
114776670 1
 
1.0%
10556910 1
 
1.0%
1227230 1
 
1.0%
57156450 1
 
1.0%
8991010 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
3040 1
1.0%
6550 1
1.0%
50370 1
1.0%
95530 1
1.0%
111240 1
1.0%
117380 1
1.0%
133200 1
1.0%
200860 1
1.0%
235110 1
1.0%
271980 1
1.0%
ValueCountFrequency (%)
114776670 1
1.0%
57782110 1
1.0%
57156450 1
1.0%
23420790 1
1.0%
21825510 1
1.0%
19536560 1
1.0%
19352490 1
1.0%
18951380 1
1.0%
18142310 1
1.0%
16012680 1
1.0%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.168317
Minimum1
Maximum1875
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T12:09:21.956664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q329
95-th percentile387
Maximum1875
Range1874
Interquartile range (IQR)27

Descriptive statistics

Standard deviation252.65364
Coefficient of variation (CV)3.0017666
Kurtosis28.513371
Mean84.168317
Median Absolute Deviation (MAD)5
Skewness4.9440008
Sum8501
Variance63833.861
MonotonicityNot monotonic
2023-12-12T12:09:22.118287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 16
15.8%
3 12
 
11.9%
2 11
 
10.9%
4 8
 
7.9%
8 6
 
5.9%
9 4
 
4.0%
6 4
 
4.0%
12 2
 
2.0%
5 2
 
2.0%
45 2
 
2.0%
Other values (30) 34
33.7%
ValueCountFrequency (%)
1 16
15.8%
2 11
10.9%
3 12
11.9%
4 8
7.9%
5 2
 
2.0%
6 4
 
4.0%
7 2
 
2.0%
8 6
 
5.9%
9 4
 
4.0%
11 2
 
2.0%
ValueCountFrequency (%)
1875 1
1.0%
1078 1
1.0%
993 1
1.0%
656 1
1.0%
570 1
1.0%
387 1
1.0%
343 1
1.0%
332 1
1.0%
293 1
1.0%
255 1
1.0%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9747964.9
Minimum3040
Maximum1.1477667 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T12:09:22.287539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3040
5-th percentile145900
Q1856650
median4249840
Q312037360
95-th percentile30986020
Maximum1.1477667 × 108
Range1.1477363 × 108
Interquartile range (IQR)11180710

Descriptive statistics

Standard deviation16389392
Coefficient of variation (CV)1.6813142
Kurtosis18.601724
Mean9747964.9
Median Absolute Deviation (MAD)3604570
Skewness3.7953478
Sum9.8454445 × 108
Variance2.6861217 × 1014
MonotonicityNot monotonic
2023-12-12T12:09:22.481743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34360 1
 
1.0%
23588920 1
 
1.0%
57782110 1
 
1.0%
26020520 1
 
1.0%
6032820 1
 
1.0%
114776670 1
 
1.0%
14806750 1
 
1.0%
1227230 1
 
1.0%
70088580 1
 
1.0%
21143620 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
3040 1
1.0%
34360 1
1.0%
50370 1
1.0%
117380 1
1.0%
145600 1
1.0%
145900 1
1.0%
287170 1
1.0%
341790 1
1.0%
370500 1
1.0%
375730 1
1.0%
ValueCountFrequency (%)
114776670 1
1.0%
70088580 1
1.0%
57782110 1
1.0%
56720120 1
1.0%
43793140 1
1.0%
30986020 1
1.0%
28662370 1
1.0%
27443600 1
1.0%
26020520 1
1.0%
24012080 1
1.0%

Interactions

2023-12-12T12:09:17.828431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:15.635383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:16.179621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:16.790185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.322348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.930172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:15.724170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:16.280659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:16.902142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.414920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:18.025026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:15.837858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:16.415741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.001502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.510040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:18.122838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:15.955375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:16.550470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.094040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.604403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:18.214618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:16.066329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:16.681153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.208962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:17.713198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:09:22.616932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
연번1.0000.9870.6760.0000.1260.1820.0420.246
과세년도0.9871.0000.0000.0000.0000.0000.0000.171
세목명0.6760.0001.0000.1140.0000.0000.2760.000
체납액구간0.0000.0000.1141.0000.0000.8520.0000.811
체납건수0.1260.0000.0000.0001.0000.2900.9700.397
체납금액0.1820.0000.0000.8520.2901.0000.2870.986
누적체납건수0.0420.0000.2760.0000.9700.2871.0000.298
누적체납금액0.2460.1710.0000.8110.3970.9860.2981.000
2023-12-12T12:09:22.748369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간과세년도
세목명1.0000.0480.000
체납액구간0.0481.0000.000
과세년도0.0000.0001.000
2023-12-12T12:09:22.909212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
연번1.000-0.1440.126-0.0230.2420.7940.4290.000
체납건수-0.1441.0000.2420.9310.2220.0000.0000.000
체납금액0.1260.2421.0000.2170.9570.0000.0000.653
누적체납건수-0.0230.9310.2171.0000.2930.0000.1650.000
누적체납금액0.2420.2220.9570.2931.0000.1010.0000.559
과세년도0.7940.0000.0000.0000.1011.0000.0000.000
세목명0.4290.0000.0000.1650.0000.0001.0000.048
체납액구간0.0000.0000.6530.0000.5590.0000.0481.000

Missing values

2023-12-12T12:09:18.348248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:09:18.505226image/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

연번시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
01전라남도장흥군468002017등록면허세10만원 미만16550234360
12전라남도장흥군468002017자동차세10만원 미만1246615029973440
23전라남도장흥군468002017자동차세10만원~30만원미만91448070183127260
34전라남도장흥군468002017재산세10만원 미만708158401291582400
45전라남도장흥군468002017주민세10만원 미만2527198040370500
56전라남도장흥군468002017지방소득세10만원 미만150370150370
67전라남도장흥군468002018등록면허세10만원 미만61112408145600
78전라남도장흥군468002018자동차세10만원 미만411651280702624720
89전라남도장흥군468002018자동차세10만원~30만원미만274718560457845820
910전라남도장흥군468002018재산세10만원 미만16424109102933993310
연번시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
9192전라남도장흥군468002021주민세10만원 미만344744002057012037360
9293전라남도장흥군468002021주민세10만원~30만원미만24259302425930
9394전라남도장흥군468002021주민세30만원~50만원미만3114356031143560
9495전라남도장흥군468002021주민세50만원~1백만원미만2188652064698780
9596전라남도장흥군468002021지방소득세10만원 미만1234431034856250
9697전라남도장흥군468002021지방소득세10만원~30만원미만113320091837040
9798전라남도장흥군468002021지방소득세1백만원~3백만원미만1153525058911340
9899전라남도장흥군468002021지역자원시설세10만원 미만1304013040
99100전라남도장흥군468002021지역자원시설세10만원~30만원미만11173801117380
100101전라남도장흥군468002021취득세10만원 미만53114206341790