Overview

Dataset statistics

Number of variables12
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory105.8 B

Variable types

Numeric5
Categorical7

Dataset

Description남양주시의 과세년도별 세목명, 체납액구간, 체납 건 수, 체납금액, 누척 체납 건 수, 누적 체납 금액으로 구성된 데이터입니다.
Author경기도 남양주시
URLhttps://www.data.go.kr/data/15102893/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
번호 is highly overall correlated with 세목명High correlation
체납건수 is highly overall correlated with 누적체납건수High correlation
체납금액 is highly overall correlated with 누적체납금액High correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액High correlation
세목명 is highly overall correlated with 번호High correlation
번호 has unique valuesUnique
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:43:49.495296
Analysis finished2023-12-12 16:43:52.795398
Duration3.3 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.5
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T01:43:52.890336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.35
Q112.75
median24.5
Q336.25
95-th percentile45.65
Maximum48
Range47
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation14
Coefficient of variation (CV)0.57142857
Kurtosis-1.2
Mean24.5
Median Absolute Deviation (MAD)12
Skewness0
Sum1176
Variance196
MonotonicityStrictly increasing
2023-12-13T01:43:53.065727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1
 
2.1%
26 1
 
2.1%
28 1
 
2.1%
29 1
 
2.1%
30 1
 
2.1%
31 1
 
2.1%
32 1
 
2.1%
33 1
 
2.1%
34 1
 
2.1%
35 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1 1
2.1%
2 1
2.1%
3 1
2.1%
4 1
2.1%
5 1
2.1%
6 1
2.1%
7 1
2.1%
8 1
2.1%
9 1
2.1%
10 1
2.1%
ValueCountFrequency (%)
48 1
2.1%
47 1
2.1%
46 1
2.1%
45 1
2.1%
44 1
2.1%
43 1
2.1%
42 1
2.1%
41 1
2.1%
40 1
2.1%
39 1
2.1%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
경기도
48 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 48
100.0%

Length

2023-12-13T01:43:53.234509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:53.325046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 48
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
남양주시
48 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남양주시 48
100.0%

Length

2023-12-13T01:43:53.446742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:53.549738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남양주시 48
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
41360
48 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41360
2nd row41360
3rd row41360
4th row41360
5th row41360

Common Values

ValueCountFrequency (%)
41360 48
100.0%

Length

2023-12-13T01:43:53.654150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:53.752009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41360 48
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
2021
48 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 48
100.0%

Length

2023-12-13T01:43:53.846404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:53.943501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 48
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size516.0 B
지방소득세
12 
재산세
10 
취득세
10 
주민세
6 
자동차세
4 
Other values (3)
6 

Length

Max length7
Median length3
Mean length3.875
Min length3

Unique

Unique1 ?
Unique (%)2.1%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 12
25.0%
재산세 10
20.8%
취득세 10
20.8%
주민세 6
12.5%
자동차세 4
 
8.3%
등록면허세 3
 
6.2%
지역자원시설세 2
 
4.2%
등록세 1
 
2.1%

Length

2023-12-13T01:43:54.057527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:54.235515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 12
25.0%
재산세 10
20.8%
취득세 10
20.8%
주민세 6
12.5%
자동차세 4
 
8.3%
등록면허세 3
 
6.2%
지역자원시설세 2
 
4.2%
등록세 1
 
2.1%

체납액구간
Categorical

Distinct12
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
10만원 미만
8 
10만원~30만원미만
7 
30만원~50만원미만
5 
50만원~1백만원미만
5 
5백만원~1천만원미만
4 
Other values (7)
19 

Length

Max length11
Median length11
Mean length10.1875
Min length7

Unique

Unique2 ?
Unique (%)4.2%

Sample

1st row10만원 미만
2nd row10만원~30만원미만
3rd row5백만원~1천만원미만
4th row10만원 미만
5th row10만원 미만

Common Values

ValueCountFrequency (%)
10만원 미만 8
16.7%
10만원~30만원미만 7
14.6%
30만원~50만원미만 5
10.4%
50만원~1백만원미만 5
10.4%
5백만원~1천만원미만 4
8.3%
1백만원~3백만원미만 4
8.3%
3백만원~5백만원미만 4
8.3%
1천만원~3천만원미만 3
 
6.2%
3천만원~5천만원미만 3
 
6.2%
5천만원~1억원미만 3
 
6.2%
Other values (2) 2
 
4.2%

Length

2023-12-13T01:43:54.457471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 8
14.3%
미만 8
14.3%
10만원~30만원미만 7
12.5%
30만원~50만원미만 5
8.9%
50만원~1백만원미만 5
8.9%
5백만원~1천만원미만 4
7.1%
1백만원~3백만원미만 4
7.1%
3백만원~5백만원미만 4
7.1%
1천만원~3천만원미만 3
 
5.4%
3천만원~5천만원미만 3
 
5.4%
Other values (3) 5
8.9%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1485.9583
Minimum1
Maximum36404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T01:43:54.686652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median41.5
Q3390.5
95-th percentile5908.7
Maximum36404
Range36403
Interquartile range (IQR)386.75

Descriptive statistics

Standard deviation5423.9582
Coefficient of variation (CV)3.6501415
Kurtosis38.386284
Mean1485.9583
Median Absolute Deviation (MAD)40.5
Skewness5.9651237
Sum71326
Variance29419323
MonotonicityNot monotonic
2023-12-13T01:43:54.908731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 5
 
10.4%
2 4
 
8.3%
3 3
 
6.2%
11 2
 
4.2%
53 2
 
4.2%
15 1
 
2.1%
461 1
 
2.1%
112 1
 
2.1%
17 1
 
2.1%
385 1
 
2.1%
Other values (27) 27
56.2%
ValueCountFrequency (%)
1 5
10.4%
2 4
8.3%
3 3
6.2%
4 1
 
2.1%
5 1
 
2.1%
9 1
 
2.1%
11 2
 
4.2%
12 1
 
2.1%
15 1
 
2.1%
17 1
 
2.1%
ValueCountFrequency (%)
36404 1
2.1%
7507 1
2.1%
6398 1
2.1%
5000 1
2.1%
3864 1
2.1%
3642 1
2.1%
3163 1
2.1%
1044 1
2.1%
916 1
2.1%
461 1
2.1%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7124573 × 108
Minimum42720
Maximum1.6444336 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T01:43:55.155457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42720
5-th percentile597778.5
Q113155675
median1.3496225 × 108
Q33.7335334 × 108
95-th percentile9.8975057 × 108
Maximum1.6444336 × 109
Range1.6443909 × 109
Interquartile range (IQR)3.6019766 × 108

Descriptive statistics

Standard deviation3.587475 × 108
Coefficient of variation (CV)1.3225923
Kurtosis3.8928402
Mean2.7124573 × 108
Median Absolute Deviation (MAD)1.3061406 × 108
Skewness1.9040782
Sum1.3019795 × 1010
Variance1.2869977 × 1017
MonotonicityNot monotonic
2023-12-13T01:43:55.385022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
48700890 1
 
2.1%
192580570 1
 
2.1%
422067880 1
 
2.1%
1644433630 1
 
2.1%
180059920 1
 
2.1%
432402310 1
 
2.1%
765147070 1
 
2.1%
659637920 1
 
2.1%
269895520 1
 
2.1%
737818800 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
42720 1
2.1%
234620 1
2.1%
480140 1
2.1%
816250 1
2.1%
1041680 1
2.1%
2271760 1
2.1%
3899820 1
2.1%
4796560 1
2.1%
6921600 1
2.1%
9071960 1
2.1%
ValueCountFrequency (%)
1644433630 1
2.1%
1128450300 1
2.1%
1046016960 1
2.1%
885255850 1
2.1%
765147070 1
2.1%
737818800 1
2.1%
665693500 1
2.1%
659637920 1
2.1%
636145970 1
2.1%
432402310 1
2.1%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4321.4167
Minimum2
Maximum85888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T01:43:55.590865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.7
Q119.75
median137.5
Q31217
95-th percentile24779.5
Maximum85888
Range85886
Interquartile range (IQR)1197.25

Descriptive statistics

Standard deviation13820.484
Coefficient of variation (CV)3.1981373
Kurtosis26.903493
Mean4321.4167
Median Absolute Deviation (MAD)131.5
Skewness4.9017934
Sum207428
Variance1.9100577 × 108
MonotonicityNot monotonic
2023-12-13T01:43:55.768897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
19 3
 
6.2%
3 2
 
4.2%
7 2
 
4.2%
106 2
 
4.2%
380 1
 
2.1%
1699 1
 
2.1%
342 1
 
2.1%
1137 1
 
2.1%
413 1
 
2.1%
2 1
 
2.1%
Other values (33) 33
68.8%
ValueCountFrequency (%)
2 1
 
2.1%
3 2
4.2%
5 1
 
2.1%
7 2
4.2%
8 1
 
2.1%
11 1
 
2.1%
17 1
 
2.1%
19 3
6.2%
20 1
 
2.1%
30 1
 
2.1%
ValueCountFrequency (%)
85888 1
2.1%
32647 1
2.1%
30971 1
2.1%
13281 1
2.1%
10232 1
2.1%
10152 1
2.1%
7850 1
2.1%
3489 1
2.1%
1733 1
2.1%
1699 1
2.1%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2274814 × 108
Minimum194860
Maximum5.6760679 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T01:43:55.940282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum194860
5-th percentile3222460
Q183318990
median4.6653854 × 108
Q31.0899016 × 109
95-th percentile3.2190341 × 109
Maximum5.6760679 × 109
Range5.675873 × 109
Interquartile range (IQR)1.0065826 × 109

Descriptive statistics

Standard deviation1.3260087 × 109
Coefficient of variation (CV)1.4370213
Kurtosis5.1978049
Mean9.2274814 × 108
Median Absolute Deviation (MAD)3.9945396 × 108
Skewness2.2525812
Sum4.4291911 × 1010
Variance1.7582991 × 1018
MonotonicityNot monotonic
2023-12-13T01:43:56.130463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
103901030 1
 
2.1%
645339960 1
 
2.1%
2608390020 1
 
2.1%
5676067910 1
 
2.1%
456301480 1
 
2.1%
1592566600 1
 
2.1%
765147070 1
 
2.1%
3018266340 1
 
2.1%
1229742090 1
 
2.1%
2696615830 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
194860 1
2.1%
348660 1
2.1%
3204050 1
2.1%
3256650 1
2.1%
13202370 1
2.1%
20512810 1
2.1%
21031600 1
2.1%
26431770 1
2.1%
37972100 1
2.1%
62354980 1
2.1%
ValueCountFrequency (%)
5676067910 1
2.1%
5567298880 1
2.1%
3327139840 1
2.1%
3018266340 1
2.1%
2911551030 1
2.1%
2696615830 1
2.1%
2608390020 1
2.1%
1741255000 1
2.1%
1592566600 1
2.1%
1415304920 1
2.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-02-07
48 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-07
2nd row2023-02-07
3rd row2023-02-07
4th row2023-02-07
5th row2023-02-07

Common Values

ValueCountFrequency (%)
2023-02-07 48
100.0%

Length

2023-12-13T01:43:56.314204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:43:56.432575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-07 48
100.0%

Interactions

2023-12-13T01:43:52.034318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:49.826101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.268409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.743111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:51.547255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:52.124912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:49.911142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.353564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.831997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:51.641756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:52.223806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.002074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.448282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.934327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:51.729047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:52.322743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.090739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.537161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:51.350207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:51.820524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:52.409834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.172010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:50.635522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:51.451484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:43:51.948476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:43:56.502894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호세목명체납액구간체납건수체납금액누적체납건수누적체납금액
번호1.0000.8990.0000.3190.3640.3810.315
세목명0.8991.0000.0000.3510.2570.6460.588
체납액구간0.0000.0001.0000.0000.0970.0000.380
체납건수0.3190.3510.0001.0000.6270.9750.551
체납금액0.3640.2570.0970.6271.0000.7320.855
누적체납건수0.3810.6460.0000.9750.7321.0000.595
누적체납금액0.3150.5880.3800.5510.8550.5951.000
2023-12-13T01:43:56.634138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.000
세목명0.0001.000
2023-12-13T01:43:56.735127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호체납건수체납금액누적체납건수누적체납금액세목명체납액구간
번호1.000-0.255-0.069-0.273-0.0910.7020.000
체납건수-0.2551.0000.4830.9470.4310.1450.000
체납금액-0.0690.4831.0000.4080.9630.0000.049
누적체납건수-0.2730.9470.4081.0000.4230.3140.000
누적체납금액-0.0910.4310.9630.4231.0000.1850.178
세목명0.7020.1450.0000.3140.1851.0000.000
체납액구간0.0000.0000.0490.0000.1780.0001.000

Missing values

2023-12-13T01:43:52.538480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:43:52.725788image/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

번호시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자
01경기도남양주시413602021등록면허세10만원 미만36424870089078501039010302023-02-07
12경기도남양주시413602021등록면허세10만원~30만원미만223462033486602023-02-07
23경기도남양주시413602021등록면허세5백만원~1천만원미만169216003210316002023-02-07
34경기도남양주시413602021등록세10만원 미만14272051948602023-02-07
45경기도남양주시413602021자동차세10만원 미만75073107044703097113356138002023-02-07
56경기도남양주시413602021자동차세10만원~30만원미만639811284503003264755672988802023-02-07
67경기도남양주시413602021자동차세30만원~50만원미만43215351672015175283805802023-02-07
78경기도남양주시413602021자동차세50만원~1백만원미만1897174501651032417102023-02-07
89경기도남양주시413602021재산세10만원 미만3163126044340132815469922502023-02-07
910경기도남양주시413602021재산세10만원~30만원미만50008852558501015217412550002023-02-07
번호시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자
3839경기도남양주시413602021취득세10만원 미만693899820313132023702023-02-07
3940경기도남양주시413602021취득세10만원~30만원미만539071960210379721002023-02-07
4041경기도남양주시413602021취득세1백만원~3백만원미만28516705101262133052202023-02-07
4142경기도남양주시413602021취득세1천만원~3천만원미만11154598980304270323602023-02-07
4243경기도남양주시413602021취득세30만원~50만원미만12479656053205128102023-02-07
4344경기도남양주시413602021취득세3백만원~5백만원미만93569331019718141802023-02-07
4445경기도남양주시413602021취득세3천만원~5천만원미만28482534072753738402023-02-07
4546경기도남양주시413602021취득세50만원~1백만원미만2013673920123861445902023-02-07
4647경기도남양주시413602021취득세5백만원~1천만원미만324133430201549332402023-02-07
4748경기도남양주시413602021취득세5천만원~1억원미만214388016074853595402023-02-07