Overview

Dataset statistics

Number of variables10
Number of observations132
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.2 KiB
Average record size in memory87.0 B

Variable types

Categorical5
Numeric5

Dataset

Description지방세 체납액 규모별 체납 건수에 대하여 과세연도, 세목명, 체납액 구간, 체납 건수, 체납 금액, 누적 체납 건수, 누적 체납 금액 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15079701/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 High correlation
체납금액 is highly overall correlated with 누적체납금액 High correlation
누적체납건수 is highly overall correlated with 체납건수 High correlation
누적체납금액 is highly overall correlated with 체납금액 High correlation
체납건수 has 8 (6.1%) zerosZeros
체납금액 has 8 (6.1%) zerosZeros

Reproduction

Analysis started2023-12-12 10:22:30.350493
Analysis finished2023-12-12 10:22:33.954619
Duration3.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
경상북도
132 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 132
100.0%

Length

2023-12-12T19:22:34.071481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:22:34.244531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 132
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
청도군
132 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청도군 132
100.0%

Length

2023-12-12T19:22:34.483887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:22:34.609535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청도군 132
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
47820
132 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47820
2nd row47820
3rd row47820
4th row47820
5th row47820

Common Values

ValueCountFrequency (%)
47820 132
100.0%

Length

2023-12-12T19:22:34.720945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:22:34.845313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47820 132
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.4773
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:22:34.999040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6781756
Coefficient of variation (CV)0.00083099505
Kurtosis-1.1985768
Mean2019.4773
Median Absolute Deviation (MAD)1
Skewness0.040026122
Sum266571
Variance2.8162734
MonotonicityIncreasing
2023-12-12T19:22:35.115751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2019 27
20.5%
2017 21
15.9%
2018 21
15.9%
2020 21
15.9%
2021 21
15.9%
2022 21
15.9%
ValueCountFrequency (%)
2017 21
15.9%
2018 21
15.9%
2019 27
20.5%
2020 21
15.9%
2021 21
15.9%
2022 21
15.9%
ValueCountFrequency (%)
2022 21
15.9%
2021 21
15.9%
2020 21
15.9%
2019 27
20.5%
2018 21
15.9%
2017 21
15.9%

세목명
Categorical

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
지방소득세
53 
재산세
26 
자동차세
19 
주민세
17 
취득세
11 

Length

Max length5
Median length4
Mean length4.0378788
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 53
40.2%
재산세 26
19.7%
자동차세 19
 
14.4%
주민세 17
 
12.9%
취득세 11
 
8.3%
등록면허세 6
 
4.5%

Length

2023-12-12T19:22:35.269757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:22:35.423230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 53
40.2%
재산세 26
19.7%
자동차세 19
 
14.4%
주민세 17
 
12.9%
취득세 11
 
8.3%
등록면허세 6
 
4.5%

체납액구간
Categorical

Distinct10
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
10만원 미만
36 
10만원~30만원미만
25 
30만원~50만원미만
19 
50만원~1백만원미만
18 
1백만원~3백만원미만
9 
Other values (5)
25 

Length

Max length11
Median length11
Mean length9.9015152
Min length7

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 36
27.3%
10만원~30만원미만 25
18.9%
30만원~50만원미만 19
14.4%
50만원~1백만원미만 18
13.6%
1백만원~3백만원미만 9
 
6.8%
1천만원~3천만원미만 7
 
5.3%
3천만원~5천만원미만 6
 
4.5%
5백만원~1천만원미만 6
 
4.5%
3백만원~5백만원미만 5
 
3.8%
5천만원~1억원미만 1
 
0.8%

Length

2023-12-12T19:22:35.572702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:22:35.737137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 36
21.4%
미만 36
21.4%
10만원~30만원미만 25
14.9%
30만원~50만원미만 19
11.3%
50만원~1백만원미만 18
10.7%
1백만원~3백만원미만 9
 
5.4%
1천만원~3천만원미만 7
 
4.2%
3천만원~5천만원미만 6
 
3.6%
5백만원~1천만원미만 6
 
3.6%
3백만원~5백만원미만 5
 
3.0%

체납건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.20455
Minimum0
Maximum3874
Zeros8
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:22:35.915725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q368
95-th percentile1406.85
Maximum3874
Range3874
Interquartile range (IQR)66

Descriptive statistics

Standard deviation578.65531
Coefficient of variation (CV)2.8198952
Kurtosis18.239244
Mean205.20455
Median Absolute Deviation (MAD)10
Skewness4.0964171
Sum27087
Variance334841.97
MonotonicityNot monotonic
2023-12-12T19:22:36.094759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
13.6%
2 13
 
9.8%
0 8
 
6.1%
3 6
 
4.5%
4 5
 
3.8%
8 4
 
3.0%
5 4
 
3.0%
11 3
 
2.3%
14 3
 
2.3%
19 3
 
2.3%
Other values (55) 65
49.2%
ValueCountFrequency (%)
0 8
6.1%
1 18
13.6%
2 13
9.8%
3 6
 
4.5%
4 5
 
3.8%
5 4
 
3.0%
6 3
 
2.3%
7 1
 
0.8%
8 4
 
3.0%
10 3
 
2.3%
ValueCountFrequency (%)
3874 1
0.8%
2859 1
0.8%
2541 1
0.8%
2155 1
0.8%
2076 1
0.8%
1742 1
0.8%
1689 1
0.8%
1176 1
0.8%
819 1
0.8%
678 1
0.8%

체납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct125
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15490774
Minimum0
Maximum1.0658763 × 108
Zeros8
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:22:36.283983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11122020
median7206965
Q318370150
95-th percentile69071692
Maximum1.0658763 × 108
Range1.0658763 × 108
Interquartile range (IQR)17248130

Descriptive statistics

Standard deviation22288184
Coefficient of variation (CV)1.4388037
Kurtosis4.6744892
Mean15490774
Median Absolute Deviation (MAD)6497640
Skewness2.166017
Sum2.0447822 × 109
Variance4.9676314 × 1014
MonotonicityNot monotonic
2023-12-12T19:22:36.426241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
6.1%
604410 1
 
0.8%
36277520 1
 
0.8%
26507190 1
 
0.8%
40255000 1
 
0.8%
7283750 1
 
0.8%
1335690 1
 
0.8%
67520 1
 
0.8%
8439260 1
 
0.8%
2325720 1
 
0.8%
Other values (115) 115
87.1%
ValueCountFrequency (%)
0 8
6.1%
23110 1
 
0.8%
41750 1
 
0.8%
67520 1
 
0.8%
79220 1
 
0.8%
138270 1
 
0.8%
157070 1
 
0.8%
278100 1
 
0.8%
293510 1
 
0.8%
326310 1
 
0.8%
ValueCountFrequency (%)
106587630 1
0.8%
106445030 1
0.8%
88696920 1
0.8%
78151780 1
0.8%
75231740 1
0.8%
71574880 1
0.8%
70709130 1
0.8%
67731970 1
0.8%
65607980 1
0.8%
53788400 1
0.8%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean640.68939
Minimum1
Maximum11732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:22:36.570827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median39.5
Q3306.5
95-th percentile3173.85
Maximum11732
Range11731
Interquartile range (IQR)295.5

Descriptive statistics

Standard deviation1731.7267
Coefficient of variation (CV)2.7029114
Kurtosis20.116947
Mean640.68939
Median Absolute Deviation (MAD)34.5
Skewness4.2686473
Sum84571
Variance2998877.3
MonotonicityNot monotonic
2023-12-12T19:22:36.774551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5
 
3.8%
8 5
 
3.8%
6 4
 
3.0%
1 4
 
3.0%
28 3
 
2.3%
11 3
 
2.3%
7 3
 
2.3%
16 3
 
2.3%
5 3
 
2.3%
13 2
 
1.5%
Other values (80) 97
73.5%
ValueCountFrequency (%)
1 4
3.0%
2 5
3.8%
3 2
 
1.5%
4 2
 
1.5%
5 3
2.3%
6 4
3.0%
7 3
2.3%
8 5
3.8%
9 2
 
1.5%
10 2
 
1.5%
ValueCountFrequency (%)
11732 1
0.8%
8852 1
0.8%
8647 1
0.8%
6592 1
0.8%
5993 1
0.8%
3838 1
0.8%
3269 1
0.8%
3096 1
0.8%
2378 1
0.8%
2276 1
0.8%

누적체납금액
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42840947
Minimum322390
Maximum3.9047668 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:22:36.947296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum322390
5-th percentile1034690
Q14556202.5
median20589245
Q355838880
95-th percentile1.2690099 × 108
Maximum3.9047668 × 108
Range3.9015429 × 108
Interquartile range (IQR)51282678

Descriptive statistics

Standard deviation59928805
Coefficient of variation (CV)1.3988674
Kurtosis11.260094
Mean42840947
Median Absolute Deviation (MAD)18127205
Skewness2.9123995
Sum5.655005 × 109
Variance3.5914617 × 1015
MonotonicityNot monotonic
2023-12-12T19:22:37.243572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1034690 3
 
2.3%
4709640 2
 
1.5%
17654130 2
 
1.5%
104231450 2
 
1.5%
1687900 1
 
0.8%
19203780 1
 
0.8%
5664670 1
 
0.8%
47513020 1
 
0.8%
56681970 1
 
0.8%
47216630 1
 
0.8%
Other values (117) 117
88.6%
ValueCountFrequency (%)
322390 1
 
0.8%
364140 1
 
0.8%
375120 1
 
0.8%
443360 1
 
0.8%
460450 1
 
0.8%
617520 1
 
0.8%
1034690 3
2.3%
1073490 1
 
0.8%
1325670 1
 
0.8%
1408690 1
 
0.8%
ValueCountFrequency (%)
390476680 1
0.8%
283889050 1
0.8%
277157920 1
0.8%
212314170 1
0.8%
198141520 1
0.8%
171963420 1
0.8%
130962680 1
0.8%
123577790 1
0.8%
116482390 1
0.8%
115226590 1
0.8%

Interactions

2023-12-12T19:22:32.973136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:30.657711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:31.461719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:31.995969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.463298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:33.084184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:30.750513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:31.556674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.080172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.558617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:33.198958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:30.852120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:31.657445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.174616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.666455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:33.304612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:30.945617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:31.752966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.260744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.767192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:33.429094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:31.371866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:31.857900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.357357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:22:32.859817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:22:37.379848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.1890.0000.110
세목명0.0001.0000.4140.3640.0000.3310.000
체납액구간0.0000.4141.0000.0000.7240.0000.434
체납건수0.0000.3640.0001.0000.6720.9260.538
체납금액0.1890.0000.7240.6721.0000.5720.820
누적체납건수0.0000.3310.0000.9260.5721.0000.516
누적체납금액0.1100.0000.4340.5380.8200.5161.000
2023-12-12T19:22:37.512248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간
세목명1.0000.227
체납액구간0.2271.000
2023-12-12T19:22:38.017546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.1860.1340.2840.1800.0000.000
체납건수0.1861.0000.4930.8640.2790.1860.000
체납금액0.1340.4931.0000.3250.8430.0000.298
누적체납건수0.2840.8640.3251.0000.2850.2020.000
누적체납금액0.1800.2790.8430.2851.0000.0000.222
세목명0.0000.1860.0000.2020.0001.0000.227
체납액구간0.0000.0000.2980.0000.2220.2271.000

Missing values

2023-12-12T19:22:33.612664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:22:33.866802image/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
0경상북도청도군478202017등록면허세10만원 미만567060501531687900
1경상북도청도군478202017자동차세10만원 미만27011498170136858009700
2경상북도청도군478202017자동차세10만원~30만원미만323530275001307212314170
3경상북도청도군478202017자동차세30만원~50만원미만41275910289644080
4경상북도청도군478202017재산세10만원 미만174221096630383851646520
5경상북도청도군478202017재산세10만원~30만원미만2032786406510554350
6경상북도청도군478202017재산세30만원~50만원미만132631093197010
7경상북도청도군478202017재산세50만원~1백만원미만154200063654530
8경상북도청도군478202017주민세10만원 미만5397602430141518376700
9경상북도청도군478202017주민세10만원~30만원미만227810081408690
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
122경상북도청도군478202022지방소득세10만원 미만6817372901343526940
123경상북도청도군478202022지방소득세10만원~30만원미만425978510759405530
124경상북도청도군478202022지방소득세1백만원~3백만원미만6553788400141102814750
125경상북도청도군478202022지방소득세1천만원~3천만원미만56560798025277157920
126경상북도청도군478202022지방소득세30만원~50만원미만1146522003310688610
127경상북도청도군478202022지방소득세3백만원~5백만원미만475465803363161790
128경상북도청도군478202022지방소득세3천만원~5천만원미만2677319706171963420
129경상북도청도군478202022지방소득세50만원~1백만원미만541375401011534070370
130경상북도청도군478202022지방소득세5백만원~1천만원미만154594280034111259980
131경상북도청도군478202022취득세10만원 미만211016180371633700