Overview

Dataset statistics

Number of variables10
Number of observations98
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory87.3 B

Variable types

Categorical6
Numeric4

Dataset

Description경상남도 거창군 지방세 체납현황에 대한 데이터로 과세년도, 세목명, 체납액구간, 체납건수, 체납금액, 누적체납건수, 누적체납금액 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079230

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
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
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:16:29.374330
Analysis finished2023-12-10 23:16:31.025995
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
경상남도
98 

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 98
100.0%

Length

2023-12-11T08:16:31.076639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:31.147453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 98
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
거창군
98 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 98
100.0%

Length

2023-12-11T08:16:31.221750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:31.312689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 98
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size916.0 B
48880
98 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 98
100.0%

Length

2023-12-11T08:16:31.408915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:31.493584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 98
100.0%

과세년도
Categorical

Distinct4
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
2020
31 
2019
27 
2018
24 
2017
16 

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 31
31.6%
2019 27
27.6%
2018 24
24.5%
2017 16
16.3%

Length

2023-12-11T08:16:31.598670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:31.703215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 31
31.6%
2019 27
27.6%
2018 24
24.5%
2017 16
16.3%

세목명
Categorical

Distinct7
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size916.0 B
지방소득세
27 
재산세
24 
자동차세
14 
주민세
14 
취득세
13 
Other values (2)
6 

Length

Max length7
Median length3
Mean length3.8571429
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 27
27.6%
재산세 24
24.5%
자동차세 14
14.3%
주민세 14
14.3%
취득세 13
13.3%
등록면허세 4
 
4.1%
지역자원시설세 2
 
2.0%

Length

2023-12-11T08:16:31.818277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:31.948381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 27
27.6%
재산세 24
24.5%
자동차세 14
14.3%
주민세 14
14.3%
취득세 13
13.3%
등록면허세 4
 
4.1%
지역자원시설세 2
 
2.0%

체납액구간
Categorical

Distinct8
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size916.0 B
10만원 미만
25 
10만원~30만원미만
16 
50만원~1백만원미만
15 
30만원~50만원미만
14 
1백만원~3백만원미만
9 
Other values (3)
19 

Length

Max length11
Median length11
Mean length9.9795918
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 25
25.5%
10만원~30만원미만 16
16.3%
50만원~1백만원미만 15
15.3%
30만원~50만원미만 14
14.3%
1백만원~3백만원미만 9
 
9.2%
3백만원~5백만원미만 7
 
7.1%
1천만원~3천만원미만 6
 
6.1%
5백만원~1천만원미만 6
 
6.1%

Length

2023-12-11T08:16:32.113853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:32.260768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 25
20.3%
미만 25
20.3%
10만원~30만원미만 16
13.0%
50만원~1백만원미만 15
12.2%
30만원~50만원미만 14
11.4%
1백만원~3백만원미만 9
 
7.3%
3백만원~5백만원미만 7
 
5.7%
1천만원~3천만원미만 6
 
4.9%
5백만원~1천만원미만 6
 
4.9%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.857143
Minimum1
Maximum2065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2023-12-11T08:16:32.406963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q326.5
95-th percentile499.9
Maximum2065
Range2064
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation285.63848
Coefficient of variation (CV)2.8604712
Kurtosis27.575185
Mean99.857143
Median Absolute Deviation (MAD)5
Skewness4.8510413
Sum9786
Variance81589.34
MonotonicityNot monotonic
2023-12-11T08:16:32.566538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 22
22.4%
2 8
 
8.2%
6 6
 
6.1%
5 5
 
5.1%
4 5
 
5.1%
3 4
 
4.1%
13 4
 
4.1%
8 3
 
3.1%
7 2
 
2.0%
58 2
 
2.0%
Other values (34) 37
37.8%
ValueCountFrequency (%)
1 22
22.4%
2 8
 
8.2%
3 4
 
4.1%
4 5
 
5.1%
5 5
 
5.1%
6 6
 
6.1%
7 2
 
2.0%
8 3
 
3.1%
9 2
 
2.0%
10 1
 
1.0%
ValueCountFrequency (%)
2065 1
1.0%
1442 1
1.0%
799 1
1.0%
694 1
1.0%
522 1
1.0%
496 1
1.0%
471 1
1.0%
358 1
1.0%
351 1
1.0%
312 1
1.0%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct98
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10606729
Minimum28420
Maximum92594940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2023-12-11T08:16:32.708707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28420
5-th percentile202115
Q1962247.5
median5543945
Q313736245
95-th percentile37766025
Maximum92594940
Range92566520
Interquartile range (IQR)12773998

Descriptive statistics

Standard deviation14903486
Coefficient of variation (CV)1.4050973
Kurtosis10.147116
Mean10606729
Median Absolute Deviation (MAD)4833935
Skewness2.7731073
Sum1.0394594 × 109
Variance2.2211388 × 1014
MonotonicityNot monotonic
2023-12-11T08:16:32.864880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202370 1
 
1.0%
19708280 1
 
1.0%
39311580 1
 
1.0%
520020 1
 
1.0%
6458670 1
 
1.0%
60123010 1
 
1.0%
23150230 1
 
1.0%
2011520 1
 
1.0%
36617111 1
 
1.0%
204780 1
 
1.0%
Other values (88) 88
89.8%
ValueCountFrequency (%)
28420 1
1.0%
70640 1
1.0%
85410 1
1.0%
101170 1
1.0%
200670 1
1.0%
202370 1
1.0%
204780 1
1.0%
224980 1
1.0%
253330 1
1.0%
291260 1
1.0%
ValueCountFrequency (%)
92594940 1
1.0%
60123010 1
1.0%
52262900 1
1.0%
50344750 1
1.0%
39311580 1
1.0%
37493280 1
1.0%
37438040 1
1.0%
36617111 1
1.0%
27537720 1
1.0%
27409910 1
1.0%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.70408
Minimum1
Maximum4416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2023-12-11T08:16:32.996840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9.5
Q358
95-th percentile1339.5
Maximum4416
Range4415
Interquartile range (IQR)55

Descriptive statistics

Standard deviation673.04477
Coefficient of variation (CV)2.7392494
Kurtosis19.605186
Mean245.70408
Median Absolute Deviation (MAD)8.5
Skewness4.1412162
Sum24079
Variance452989.26
MonotonicityNot monotonic
2023-12-11T08:16:33.133803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
2 11
 
11.2%
1 10
 
10.2%
3 7
 
7.1%
8 6
 
6.1%
5 5
 
5.1%
9 4
 
4.1%
7 4
 
4.1%
17 3
 
3.1%
36 2
 
2.0%
25 2
 
2.0%
Other values (39) 44
44.9%
ValueCountFrequency (%)
1 10
10.2%
2 11
11.2%
3 7
7.1%
4 2
 
2.0%
5 5
5.1%
7 4
 
4.1%
8 6
6.1%
9 4
 
4.1%
10 1
 
1.0%
11 1
 
1.0%
ValueCountFrequency (%)
4416 1
1.0%
3320 1
1.0%
2351 1
1.0%
1878 1
1.0%
1552 1
1.0%
1302 1
1.0%
1184 1
1.0%
1056 1
1.0%
1055 1
1.0%
780 1
1.0%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct98
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20207260
Minimum85410
Maximum1.777738 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1014.0 B
2023-12-11T08:16:33.549910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85410
5-th percentile279726
Q12142477.5
median10817785
Q324032588
95-th percentile66990503
Maximum1.777738 × 108
Range1.7768839 × 108
Interquartile range (IQR)21890110

Descriptive statistics

Standard deviation29275339
Coefficient of variation (CV)1.4487535
Kurtosis10.564126
Mean20207260
Median Absolute Deviation (MAD)9616400
Skewness2.8820434
Sum1.9803115 × 109
Variance8.5704547 × 1014
MonotonicityNot monotonic
2023-12-11T08:16:33.721418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
582840 1
 
1.0%
40223870 1
 
1.0%
74397590 1
 
1.0%
1620930 1
 
1.0%
17856680 1
 
1.0%
177773800 1
 
1.0%
59625090 1
 
1.0%
3591280 1
 
1.0%
66417331 1
 
1.0%
215300 1
 
1.0%
Other values (88) 88
89.8%
ValueCountFrequency (%)
85410 1
1.0%
101170 1
1.0%
215300 1
1.0%
224980 1
1.0%
243720 1
1.0%
286080 1
1.0%
356720 1
1.0%
392430 1
1.0%
407220 1
1.0%
410710 1
1.0%
ValueCountFrequency (%)
177773800 1
1.0%
133754050 1
1.0%
117650790 1
1.0%
74397590 1
1.0%
70238480 1
1.0%
66417331 1
1.0%
65792470 1
1.0%
65387890 1
1.0%
61198590 1
1.0%
59625090 1
1.0%

Interactions

2023-12-11T08:16:30.569592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:29.690989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.029889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.302780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.633612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:29.760749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.091322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.369024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.700785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:29.866475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.158626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.436352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.766821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:29.941856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.224667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:30.500468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:16:33.812536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0830.2010.0000.253
세목명0.0001.0000.0000.0000.0000.0660.000
체납액구간0.0000.0001.0000.0000.5590.0000.214
체납건수0.0830.0000.0001.0000.5110.9650.738
체납금액0.2010.0000.5590.5111.0000.6120.968
누적체납건수0.0000.0660.0000.9650.6121.0000.860
누적체납금액0.2530.0000.2140.7380.9680.8601.000
2023-12-11T08:16:33.906462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2023-12-11T08:16:33.999139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.3960.9740.4960.0470.0000.000
체납금액0.3961.0000.3560.9660.0690.0000.216
누적체납건수0.9740.3561.0000.4910.0000.0210.000
누적체납금액0.4960.9660.4911.0000.1180.0000.070
과세년도0.0470.0690.0000.1181.0000.0000.000
세목명0.0000.0000.0210.0000.0001.0000.000
체납액구간0.0000.2160.0000.0700.0000.0001.000

Missing values

2023-12-11T08:16:30.866286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:16:30.976684image/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
0경상남도거창군488802017등록면허세10만원 미만2020237058582840
1경상남도거창군488802017자동차세10만원 미만117543481034716208910
2경상남도거창군488802017자동차세10만원~30만원미만871451899022637850170
3경상남도거창군488802017자동차세30만원~50만원미만5171271093172220
4경상남도거창군488802017재산세10만원 미만3585134060105614767010
5경상남도거창군488802017재산세10만원~30만원미만152583980387013920
6경상남도거창군488802017재산세50만원~1백만원미만16222401622240
7경상남도거창군488802017주민세10만원 미만302440891071310342100
8경상남도거창군488802017주민세50만원~1백만원미만16962301696230
9경상남도거창군488802017지방소득세10만원 미만14484050271195970
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
88경상남도거창군488802020지방소득세3백만원~5백만원미만5187267101554595240
89경상남도거창군488802020지방소득세50만원~1백만원미만1496138303626123770
90경상남도거창군488802020지방소득세5백만원~1천만원미만750344750965792470
91경상남도거창군488802020지역자원시설세10만원 미만1284208243720
92경상남도거창군488802020취득세10만원 미만3706408356720
93경상남도거창군488802020취득세1백만원~3백만원미만13236507201323650720
94경상남도거창군488802020취득세30만원~50만원미만14523202859540
95경상남도거창군488802020취득세3백만원~5백만원미만311159500311159500
96경상남도거창군488802020취득세50만원~1백만원미만8626699086266990
97경상남도거창군488802020취득세5백만원~1천만원미만15719650212953900