Overview

Dataset statistics

Number of variables10
Number of observations230
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.4 KiB
Average record size in memory86.6 B

Variable types

Categorical5
Numeric5

Dataset

Description울산광역시 남구 지방세 체납 현황에 대한 데이터로 시도명, 시군구명, 자치단체코드, 과세년도, 세목명, 체납액구간, 체납건수, 체납금액 등의 항목을 제공합니다.
Author울산광역시 남구
URLhttps://www.data.go.kr/data/15078402/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수 and 1 other fieldsHigh correlation
체납금액 is highly overall correlated with 누적체납금액High correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:42:39.791006
Analysis finished2024-04-29 22:42:43.847496
Duration4.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
울산광역시
230 

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 230
100.0%

Length

2024-04-30T07:42:43.913099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:44.001966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산광역시 230
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
남구
230 

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 230
100.0%

Length

2024-04-30T07:42:44.106197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:44.183756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남구 230
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
31140
230 

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 230
100.0%

Length

2024-04-30T07:42:44.285234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:44.389047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
31140 230
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.6348
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-30T07:42:44.469189image/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 deviation1.6942257
Coefficient of variation (CV)0.00083887725
Kurtosis-1.2381642
Mean2019.6348
Median Absolute Deviation (MAD)1
Skewness-0.098372261
Sum464516
Variance2.8704006
MonotonicityDecreasing
2024-04-30T07:42:44.607378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 42
18.3%
2021 41
17.8%
2020 40
17.4%
2019 38
16.5%
2018 36
15.7%
2017 33
14.3%
ValueCountFrequency (%)
2017 33
14.3%
2018 36
15.7%
2019 38
16.5%
2020 40
17.4%
2021 41
17.8%
2022 42
18.3%
ValueCountFrequency (%)
2022 42
18.3%
2021 41
17.8%
2020 40
17.4%
2019 38
16.5%
2018 36
15.7%
2017 33
14.3%

세목명
Categorical

Distinct7
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
지방소득세
63 
재산세
52 
취득세
45 
주민세
28 
자동차세
24 
Other values (2)
18 

Length

Max length7
Median length3
Mean length3.8695652
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 63
27.4%
재산세 52
22.6%
취득세 45
19.6%
주민세 28
12.2%
자동차세 24
 
10.4%
등록면허세 11
 
4.8%
지역자원시설세 7
 
3.0%

Length

2024-04-30T07:42:44.724689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:42:44.836252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 63
27.4%
재산세 52
22.6%
취득세 45
19.6%
주민세 28
12.2%
자동차세 24
 
10.4%
등록면허세 11
 
4.8%
지역자원시설세 7
 
3.0%

체납액구간
Categorical

Distinct11
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
10만원 미만
42 
10만원~30만원미만
35 
30만원~50만원미만
29 
50만원~1백만원미만
28 
1백만원~3백만원미만
21 
Other values (6)
75 

Length

Max length11
Median length11
Mean length10.165217
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원 미만
2nd row10만원~30만원미만
3rd row10만원 미만
4th row10만원~30만원미만
5th row30만원~50만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 42
18.3%
10만원~30만원미만 35
15.2%
30만원~50만원미만 29
12.6%
50만원~1백만원미만 28
12.2%
1백만원~3백만원미만 21
9.1%
5백만원~1천만원미만 17
7.4%
3백만원~5백만원미만 16
 
7.0%
1천만원~3천만원미만 14
 
6.1%
3천만원~5천만원미만 11
 
4.8%
5천만원~1억원미만 10
 
4.3%

Length

2024-04-30T07:42:44.967265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 42
15.4%
미만 42
15.4%
10만원~30만원미만 35
12.9%
30만원~50만원미만 29
10.7%
50만원~1백만원미만 28
10.3%
1백만원~3백만원미만 21
7.7%
5백만원~1천만원미만 17
6.2%
3백만원~5백만원미만 16
 
5.9%
1천만원~3천만원미만 14
 
5.1%
3천만원~5천만원미만 11
 
4.0%
Other values (2) 17
6.2%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498.66522
Minimum1
Maximum13873
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-30T07:42:45.096498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median16
Q3151.75
95-th percentile2173.05
Maximum13873
Range13872
Interquartile range (IQR)148.75

Descriptive statistics

Standard deviation1667.1176
Coefficient of variation (CV)3.3431601
Kurtosis37.921531
Mean498.66522
Median Absolute Deviation (MAD)15
Skewness5.8425307
Sum114693
Variance2779281.2
MonotonicityNot monotonic
2024-04-30T07:42:45.407223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 33
 
14.3%
3 15
 
6.5%
2 14
 
6.1%
4 11
 
4.8%
7 7
 
3.0%
5 7
 
3.0%
9 6
 
2.6%
6 6
 
2.6%
8 4
 
1.7%
44 3
 
1.3%
Other values (102) 124
53.9%
ValueCountFrequency (%)
1 33
14.3%
2 14
6.1%
3 15
6.5%
4 11
 
4.8%
5 7
 
3.0%
6 6
 
2.6%
7 7
 
3.0%
8 4
 
1.7%
9 6
 
2.6%
10 3
 
1.3%
ValueCountFrequency (%)
13873 1
0.4%
11898 1
0.4%
11798 1
0.4%
8002 1
0.4%
6846 1
0.4%
5170 1
0.4%
2866 1
0.4%
2658 1
0.4%
2416 1
0.4%
2375 1
0.4%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct230
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0736604 × 108
Minimum1930
Maximum9.734041 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-30T07:42:45.560363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1930
5-th percentile237636
Q14579172.5
median45840305
Q31.4524276 × 108
95-th percentile3.9275308 × 108
Maximum9.734041 × 108
Range9.7340217 × 108
Interquartile range (IQR)1.4066359 × 108

Descriptive statistics

Standard deviation1.5407518 × 108
Coefficient of variation (CV)1.4350458
Kurtosis8.9432501
Mean1.0736604 × 108
Median Absolute Deviation (MAD)44810385
Skewness2.6141696
Sum2.4694189 × 1010
Variance2.3739161 × 1016
MonotonicityNot monotonic
2024-04-30T07:42:45.710050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39856670 1
 
0.4%
48382100 1
 
0.4%
365789120 1
 
0.4%
85499830 1
 
0.4%
379436450 1
 
0.4%
331480110 1
 
0.4%
40890 1
 
0.4%
256160 1
 
0.4%
611290 1
 
0.4%
8786700 1
 
0.4%
Other values (220) 220
95.7%
ValueCountFrequency (%)
1930 1
0.4%
16220 1
0.4%
27260 1
0.4%
40890 1
0.4%
45610 1
0.4%
63000 1
0.4%
110350 1
0.4%
111430 1
0.4%
129200 1
0.4%
139050 1
0.4%
ValueCountFrequency (%)
973404100 1
0.4%
861735120 1
0.4%
828789230 1
0.4%
729351680 1
0.4%
545774550 1
0.4%
528952330 1
0.4%
487750000 1
0.4%
465782820 1
0.4%
457662350 1
0.4%
416526400 1
0.4%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct159
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1677.6435
Minimum1
Maximum42666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-30T07:42:45.848292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q115.25
median76.5
Q3585
95-th percentile8622.2
Maximum42666
Range42665
Interquartile range (IQR)569.75

Descriptive statistics

Standard deviation5206.0749
Coefficient of variation (CV)3.103207
Kurtosis31.080915
Mean1677.6435
Median Absolute Deviation (MAD)73
Skewness5.2148722
Sum385858
Variance27103216
MonotonicityNot monotonic
2024-04-30T07:42:45.978321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 8
 
3.5%
1 8
 
3.5%
7 6
 
2.6%
3 6
 
2.6%
37 5
 
2.2%
9 5
 
2.2%
47 5
 
2.2%
20 4
 
1.7%
6 4
 
1.7%
14 4
 
1.7%
Other values (149) 175
76.1%
ValueCountFrequency (%)
1 8
3.5%
2 8
3.5%
3 6
2.6%
4 4
1.7%
5 2
 
0.9%
6 4
1.7%
7 6
2.6%
8 2
 
0.9%
9 5
2.2%
10 1
 
0.4%
ValueCountFrequency (%)
42666 1
0.4%
33944 1
0.4%
32706 1
0.4%
28793 1
0.4%
20791 1
0.4%
13945 1
0.4%
13369 1
0.4%
12717 1
0.4%
10503 1
0.4%
10342 1
0.4%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct230
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0196082 × 108
Minimum61750
Maximum2.4963186 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2024-04-30T07:42:46.109410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61750
5-th percentile1592850
Q131971335
median1.2583292 × 108
Q33.6094933 × 108
95-th percentile1.2692566 × 109
Maximum2.4963186 × 109
Range2.4962568 × 109
Interquartile range (IQR)3.2897799 × 108

Descriptive statistics

Standard deviation4.3486502 × 108
Coefficient of variation (CV)1.4401373
Kurtosis6.2410035
Mean3.0196082 × 108
Median Absolute Deviation (MAD)1.1161724 × 108
Skewness2.3744625
Sum6.9450988 × 1010
Variance1.8910759 × 1017
MonotonicityNot monotonic
2024-04-30T07:42:46.268852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107723910 1
 
0.4%
123449170 1
 
0.4%
824076110 1
 
0.4%
355681670 1
 
0.4%
945636510 1
 
0.4%
807825390 1
 
0.4%
148250 1
 
0.4%
4837930 1
 
0.4%
13750100 1
 
0.4%
76306700 1
 
0.4%
Other values (220) 220
95.7%
ValueCountFrequency (%)
61750 1
0.4%
107360 1
0.4%
111430 1
0.4%
148250 1
0.4%
175510 1
0.4%
245820 1
0.4%
250480 1
0.4%
304710 1
0.4%
455530 1
0.4%
566770 1
0.4%
ValueCountFrequency (%)
2496318570 1
0.4%
2252335600 1
0.4%
2144723800 1
0.4%
1793279360 1
0.4%
1728401440 1
0.4%
1504067980 1
0.4%
1418282100 1
0.4%
1367770180 1
0.4%
1348151450 1
0.4%
1297373480 1
0.4%

Interactions

2024-04-30T07:42:43.199421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:41.309218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:41.927241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.362387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.767642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:43.275613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:41.435203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.027314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.448059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.853907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:43.358159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:41.658605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.114830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.538744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.936778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:43.452228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:41.760833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.197983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.615495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:43.008257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:43.528110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:41.843791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.275765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:42.691812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:42:43.103766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:42:46.367594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.2460.0000.000
세목명0.0001.0000.3000.3170.2840.4200.414
체납액구간0.0000.3001.0000.0000.5090.0500.335
체납건수0.0000.3170.0001.0000.2590.9940.473
체납금액0.2460.2840.5090.2591.0000.1830.822
누적체납건수0.0000.4200.0500.9940.1831.0000.599
누적체납금액0.0000.4140.3350.4730.8220.5991.000
2024-04-30T07:42:46.465765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.151
세목명0.1511.000
2024-04-30T07:42:46.546525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.0350.185-0.0240.1410.0000.000
체납건수0.0351.0000.4910.9300.5610.1750.000
체납금액0.1850.4911.0000.3010.9510.1540.258
누적체납건수-0.0240.9300.3011.0000.4490.2400.019
누적체납금액0.1410.5610.9510.4491.0000.2230.149
세목명0.0000.1750.1540.2400.2231.0000.151
체납액구간0.0000.0000.2580.0190.1490.1511.000

Missing values

2024-04-30T07:42:43.642680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:42:43.781603image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
0울산광역시남구311402022등록면허세10만원 미만1113398566702978107723910
1울산광역시남구311402022등록면허세10만원~30만원미만11390502250480
2울산광역시남구311402022자동차세10만원 미만24161031522008238350204750
3울산광역시남구311402022자동차세10만원~30만원미만186832285469082191418282100
4울산광역시남구311402022자동차세30만원~50만원미만11640532730454154395570
5울산광역시남구311402022자동차세50만원~1백만원미만528627304627312010
6울산광역시남구311402022재산세10만원 미만1316570023504490169808820
7울산광역시남구311402022재산세10만원~30만원미만18173240884903707669703970
8울산광역시남구311402022재산세1백만원~3백만원미만125209688790268442244430
9울산광역시남구311402022재산세1억원~3억원미만11111686301111168630
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
220울산광역시남구311402017지방소득세30만원~50만원미만40169714008032850250
221울산광역시남구311402017지방소득세3백만원~5백만원미만2912529869081323313820
222울산광역시남구311402017지방소득세3천만원~5천만원미만1498481406225280540
223울산광역시남구311402017지방소득세50만원~1백만원미만10172617370270192664340
224울산광역시남구311402017지방소득세5백만원~1천만원미만2013878354053362848500
225울산광역시남구311402017지역자원시설세10만원 미만11930261750
226울산광역시남구311402017취득세10만원 미만32169101063997770
227울산광역시남구311402017취득세10만원~30만원미만510209606511186710
228울산광역시남구311402017취득세1백만원~3백만원미만361782003762436860
229울산광역시남구311402017취득세50만원~1백만원미만322195503726452980