Overview

Dataset statistics

Number of variables10
Number of observations120
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory87.1 B

Variable types

Categorical6
Numeric4

Dataset

Description2017년부터 2019년 지방세 세목별 체납규모별 체납건수, 체납금액 데이터로 체납규모별 체납건수를 납세자 유형별로 데이터를 제공하여 체납정책 수립시 기초자료로 활용
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079428

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(원) is highly overall correlated with 누적체납금액(원)High correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액(원) is highly overall correlated with 체납금액(원)High correlation
체납금액(원) has unique valuesUnique
누적체납금액(원) has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:29:17.453009
Analysis finished2023-12-11 00:29:19.494085
Duration2.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
경상남도
120 

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 120
100.0%

Length

2023-12-11T09:29:19.561648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:29:19.640026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 120
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
양산시
120 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양산시 120
100.0%

Length

2023-12-11T09:29:19.726063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:29:19.805026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
양산시 120
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
48330
120 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48330
2nd row48330
3rd row48330
4th row48330
5th row48330

Common Values

ValueCountFrequency (%)
48330 120
100.0%

Length

2023-12-11T09:29:19.893019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:29:19.999078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48330 120
100.0%

과세년도
Categorical

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2019
45 
2018
39 
2017
36 

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 (%)
2019 45
37.5%
2018 39
32.5%
2017 36
30.0%

Length

2023-12-11T09:29:20.091969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:29:20.173287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 45
37.5%
2018 39
32.5%
2017 36
30.0%

세목명
Categorical

Distinct7
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
지방소득세
29 
취득세
29 
재산세
26 
주민세
15 
자동차세
12 
Other values (2)
9 

Length

Max length7
Median length3
Mean length3.8166667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 29
24.2%
취득세 29
24.2%
재산세 26
21.7%
주민세 15
12.5%
자동차세 12
10.0%
지역자원시설세 5
 
4.2%
등록면허세 4
 
3.3%

Length

2023-12-11T09:29:20.289259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:29:20.415986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 29
24.2%
취득세 29
24.2%
재산세 26
21.7%
주민세 15
12.5%
자동차세 12
10.0%
지역자원시설세 5
 
4.2%
등록면허세 4
 
3.3%

체납액구간
Categorical

Distinct11
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
10만원 미만
19 
30만원~50만원미만
17 
50만원~1백만원미만
17 
10만원~30만원미만
16 
1백만원~3백만원미만
12 
Other values (6)
39 

Length

Max length11
Median length11
Mean length10.258333
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row50만원~1백만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 19
15.8%
30만원~50만원미만 17
14.2%
50만원~1백만원미만 17
14.2%
10만원~30만원미만 16
13.3%
1백만원~3백만원미만 12
10.0%
3백만원~5백만원미만 9
7.5%
5백만원~1천만원미만 9
7.5%
1천만원~3천만원미만 8
6.7%
5천만원~1억원미만 5
 
4.2%
1억원~3억원미만 4
 
3.3%

Length

2023-12-11T09:29:20.584446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 19
13.7%
미만 19
13.7%
30만원~50만원미만 17
12.2%
50만원~1백만원미만 17
12.2%
10만원~30만원미만 16
11.5%
1백만원~3백만원미만 12
8.6%
3백만원~5백만원미만 9
6.5%
5백만원~1천만원미만 9
6.5%
1천만원~3천만원미만 8
5.8%
5천만원~1억원미만 5
 
3.6%
Other values (2) 8
5.8%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean491.8
Minimum1
Maximum9102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T09:29:20.755324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median10
Q3117.25
95-th percentile2947.3
Maximum9102
Range9101
Interquartile range (IQR)114.25

Descriptive statistics

Standard deviation1425.6869
Coefficient of variation (CV)2.898916
Kurtosis18.02385
Mean491.8
Median Absolute Deviation (MAD)9
Skewness4.0477319
Sum59016
Variance2032583.2
MonotonicityNot monotonic
2023-12-11T09:29:20.874856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
15.0%
6 10
 
8.3%
3 9
 
7.5%
2 6
 
5.0%
4 6
 
5.0%
10 4
 
3.3%
7 3
 
2.5%
33 2
 
1.7%
23 2
 
1.7%
8 2
 
1.7%
Other values (51) 58
48.3%
ValueCountFrequency (%)
1 18
15.0%
2 6
 
5.0%
3 9
7.5%
4 6
 
5.0%
5 2
 
1.7%
6 10
8.3%
7 3
 
2.5%
8 2
 
1.7%
9 1
 
0.8%
10 4
 
3.3%
ValueCountFrequency (%)
9102 1
0.8%
7622 1
0.8%
6402 1
0.8%
4663 1
0.8%
3516 1
0.8%
3219 1
0.8%
2933 1
0.8%
2728 1
0.8%
2625 1
0.8%
2547 1
0.8%

체납금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71817938
Minimum59980
Maximum8.0314084 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T09:29:21.007909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum59980
5-th percentile491609
Q15275835
median35194605
Q397262798
95-th percentile2.4579256 × 108
Maximum8.0314084 × 108
Range8.0308086 × 108
Interquartile range (IQR)91986962

Descriptive statistics

Standard deviation1.091461 × 108
Coefficient of variation (CV)1.519761
Kurtosis18.241498
Mean71817938
Median Absolute Deviation (MAD)32893410
Skewness3.5835086
Sum8.6181525 × 109
Variance1.1912872 × 1016
MonotonicityNot monotonic
2023-12-11T09:29:21.175758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7564800 1
 
0.8%
74580720 1
 
0.8%
115231080 1
 
0.8%
73397290 1
 
0.8%
52744870 1
 
0.8%
146757800 1
 
0.8%
85217230 1
 
0.8%
50615160 1
 
0.8%
154470010 1
 
0.8%
140874420 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
59980 1
0.8%
115870 1
0.8%
169940 1
0.8%
307100 1
0.8%
421330 1
0.8%
440100 1
0.8%
494320 1
0.8%
509980 1
0.8%
522380 1
0.8%
643190 1
0.8%
ValueCountFrequency (%)
803140840 1
0.8%
495302650 1
0.8%
370061670 1
0.8%
348383190 1
0.8%
339169810 1
0.8%
257392170 1
0.8%
245182050 1
0.8%
243221250 1
0.8%
229034950 1
0.8%
178957290 1
0.8%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1895.3667
Minimum1
Maximum34430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T09:29:21.291785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median61
Q3322.5
95-th percentile14032.5
Maximum34430
Range34429
Interquartile range (IQR)310.5

Descriptive statistics

Standard deviation5426.4004
Coefficient of variation (CV)2.8629819
Kurtosis14.703803
Mean1895.3667
Median Absolute Deviation (MAD)57
Skewness3.6744761
Sum227444
Variance29445821
MonotonicityNot monotonic
2023-12-11T09:29:21.416597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 5
 
4.2%
1 4
 
3.3%
6 3
 
2.5%
42 3
 
2.5%
2 3
 
2.5%
3 3
 
2.5%
12 3
 
2.5%
5 2
 
1.7%
19 2
 
1.7%
33 2
 
1.7%
Other values (82) 90
75.0%
ValueCountFrequency (%)
1 4
3.3%
2 3
2.5%
3 3
2.5%
4 5
4.2%
5 2
 
1.7%
6 3
2.5%
7 1
 
0.8%
8 1
 
0.8%
9 2
 
1.7%
10 2
 
1.7%
ValueCountFrequency (%)
34430 1
0.8%
25328 1
0.8%
18888 1
0.8%
18625 1
0.8%
17706 1
0.8%
15372 1
0.8%
13962 1
0.8%
13292 1
0.8%
12747 1
0.8%
11029 1
0.8%

누적체납금액(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4705483 × 108
Minimum115870
Maximum3.1009157 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T09:29:21.557293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum115870
5-th percentile2723893
Q134312265
median96205920
Q33.2037968 × 108
95-th percentile6.9928075 × 108
Maximum3.1009157 × 109
Range3.1007998 × 109
Interquartile range (IQR)2.8606742 × 108

Descriptive statistics

Standard deviation4.15377 × 108
Coefficient of variation (CV)1.681315
Kurtosis23.860529
Mean2.4705483 × 108
Median Absolute Deviation (MAD)87164880
Skewness4.3408471
Sum2.964658 × 1010
Variance1.7253805 × 1017
MonotonicityNot monotonic
2023-12-11T09:29:21.698893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19810280 1
 
0.8%
139758030 1
 
0.8%
115231080 1
 
0.8%
205722130 1
 
0.8%
138981170 1
 
0.8%
146757800 1
 
0.8%
232228050 1
 
0.8%
95558320 1
 
0.8%
670632010 1
 
0.8%
466247190 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
115870 1
0.8%
307100 1
0.8%
461670 1
0.8%
522380 1
0.8%
1096370 1
0.8%
1165570 1
0.8%
2805910 1
0.8%
4369640 1
0.8%
4756560 1
0.8%
5510170 1
0.8%
ValueCountFrequency (%)
3100915670 1
0.8%
2297774830 1
0.8%
1802472180 1
0.8%
992472270 1
0.8%
852544720 1
0.8%
749251020 1
0.8%
696650740 1
0.8%
694204840 1
0.8%
670632010 1
0.8%
668304690 1
0.8%

Interactions

2023-12-11T09:29:18.724485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:17.745842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.063416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.405541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.802256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:17.806947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.146706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.475802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:19.143914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:17.908487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.234459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.563163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:19.224206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:17.987874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.324352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:29:18.645986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:29:21.781101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액(원)누적체납건수누적체납금액(원)
과세년도1.0000.0000.0000.0710.0740.1040.148
세목명0.0001.0000.0000.3330.3890.5400.298
체납액구간0.0000.0001.0000.0000.3690.0000.064
체납건수0.0710.3330.0001.0000.7380.9120.721
체납금액(원)0.0740.3890.3690.7381.0000.7730.970
누적체납건수0.1040.5400.0000.9120.7731.0000.838
누적체납금액(원)0.1480.2980.0640.7210.9700.8381.000
2023-12-11T09:29:21.867186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명과세년도
체납액구간1.0000.0000.000
세목명0.0001.0000.000
과세년도0.0000.0001.000
2023-12-11T09:29:21.942955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액(원)누적체납건수누적체납금액(원)과세년도세목명체납액구간
체납건수1.0000.4300.9350.4110.0380.1830.000
체납금액(원)0.4301.0000.2720.9400.0000.1450.187
누적체납건수0.9350.2721.0000.3230.0660.2120.000
누적체납금액(원)0.4110.9400.3231.0000.0960.1100.000
과세년도0.0380.0000.0660.0961.0000.0000.000
세목명0.1830.1450.2120.1100.0001.0000.000
체납액구간0.0000.1870.0000.0000.0000.0001.000

Missing values

2023-12-11T09:29:19.324173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:29:19.440484image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액(원)누적체납건수누적체납금액(원)
0경상남도양산시483302017등록면허세10만원 미만4657564800127919810280
1경상남도양산시483302017자동차세10만원 미만21559308029012747582562620
2경상남도양산시483302017자동차세10만원~30만원미만2223370061670110291802472180
3경상남도양산시483302017자동차세30만원~50만원미만541814539028096853520
4경상남도양산시483302017자동차세50만원~1백만원미만632926504226285390
5경상남도양산시483302017재산세10만원 미만2547678003407345228111840
6경상남도양산시483302017재산세10만원~30만원미만360518921701101165086190
7경상남도양산시483302017재산세1백만원~3백만원미만2545361080135237880820
8경상남도양산시483302017재산세1천만원~3천만원미만35257500028472078400
9경상남도양산시483302017재산세30만원~50만원미만1343428509633603470
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액(원)누적체납건수누적체납금액(원)
110경상남도양산시483302019취득세10만원~30만원미만611905906812535720
111경상남도양산시483302019취득세1백만원~3백만원미만6122837304273672270
112경상남도양산시483302019취득세1억원~3억원미만11207202105668304690
113경상남도양산시483302019취득세1천만원~3천만원미만110048920686092980
114경상남도양산시483302019취득세30만원~50만원미만27114847403915854380
115경상남도양산시483302019취득세3백만원~5백만원미만145824101039683740
116경상남도양산시483302019취득세3천만원~5천만원미만282013980282013980
117경상남도양산시483302019취득세50만원~1백만원미만320135204934548530
118경상남도양산시483302019취득세5백만원~1천만원미만164330801172476020
119경상남도양산시483302019취득세5천만원~1억원미만21239492906390568590