Overview

Dataset statistics

Number of variables10
Number of observations259
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.9 KiB
Average record size in memory86.5 B

Variable types

Categorical5
Numeric5

Dataset

Description세목별, 체납액 구간별 지방세 체납건수 및 체납금액 데이터를 제공합니다. 지방세 체납 정책 수립시 기초자료로 활용할 수 있습니다.
Author충청남도 아산시
URLhttps://www.data.go.kr/data/15079078/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 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액 High correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2024-05-04 07:47:42.703258
Analysis finished2024-05-04 07:47:52.874068
Duration10.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
충청남도
259 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 259
100.0%

Length

2024-05-04T07:47:53.172745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:47:53.590544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 259
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
아산시
259 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아산시 259
100.0%

Length

2024-05-04T07:47:54.023322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:47:54.344845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아산시 259
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
44200
259 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44200
2nd row44200
3rd row44200
4th row44200
5th row44200

Common Values

ValueCountFrequency (%)
44200 259
100.0%

Length

2024-05-04T07:47:54.680966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:47:54.973273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44200 259
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5907
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:47:55.462781image/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 deviation1.696574
Coefficient of variation (CV)0.00084005833
Kurtosis-1.253415
Mean2019.5907
Median Absolute Deviation (MAD)1
Skewness-0.056359694
Sum523074
Variance2.8783634
MonotonicityIncreasing
2024-05-04T07:47:55.789448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 46
17.8%
2021 45
17.4%
2020 44
17.0%
2018 43
16.6%
2019 43
16.6%
2017 38
14.7%
ValueCountFrequency (%)
2017 38
14.7%
2018 43
16.6%
2019 43
16.6%
2020 44
17.0%
2021 45
17.4%
2022 46
17.8%
ValueCountFrequency (%)
2022 46
17.8%
2021 45
17.4%
2020 44
17.0%
2019 43
16.6%
2018 43
16.6%
2017 38
14.7%

세목명
Categorical

Distinct14
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
지방소득세
43 
취득세
38 
재산세
35 
주민세
25 
지방소득세
22 
Other values (9)
96 

Length

Max length9
Median length7
Mean length4.5135135
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 43
16.6%
취득세 38
14.7%
재산세 35
13.5%
주민세 25
9.7%
지방소득세 22
8.5%
취득세 21
8.1%
재산세 19
7.3%
자동차세 16
 
6.2%
주민세 12
 
4.6%
등록면허세 8
 
3.1%
Other values (4) 20
7.7%

Length

2024-05-04T07:47:56.213959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방소득세 65
25.1%
취득세 59
22.8%
재산세 54
20.8%
주민세 37
14.3%
자동차세 24
 
9.3%
등록면허세 10
 
3.9%
지역자원시설세 10
 
3.9%

체납액구간
Categorical

Distinct22
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
10만원 미만
26 
10만원~30만원미만
22 
50만원~1백만원미만
21 
30만원~50만원미만
20 
1백만원~3백만원미만
17 
Other values (17)
153 

Length

Max length13
Median length11
Mean length10.965251
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 26
 
10.0%
10만원~30만원미만 22
 
8.5%
50만원~1백만원미만 21
 
8.1%
30만원~50만원미만 20
 
7.7%
1백만원~3백만원미만 17
 
6.6%
5백만원~1천만원미만 16
 
6.2%
10만원 미만 14
 
5.4%
3백만원~5백만원미만 14
 
5.4%
10만원~30만원미만 12
 
4.6%
1천만원~3천만원미만 12
 
4.6%
Other values (12) 85
32.8%

Length

2024-05-04T07:47:56.867464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 40
13.4%
미만 40
13.4%
10만원~30만원미만 34
11.4%
50만원~1백만원미만 31
10.4%
30만원~50만원미만 30
10.0%
1백만원~3백만원미만 26
8.7%
5백만원~1천만원미만 22
7.4%
3백만원~5백만원미만 22
7.4%
1천만원~3천만원미만 18
6.0%
3천만원~5천만원미만 16
 
5.4%
Other values (2) 20
6.7%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean739.16988
Minimum1
Maximum16449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:47:57.464637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median17
Q3222
95-th percentile4812.4
Maximum16449
Range16448
Interquartile range (IQR)218

Descriptive statistics

Standard deviation2213.6837
Coefficient of variation (CV)2.994824
Kurtosis26.929716
Mean739.16988
Median Absolute Deviation (MAD)16
Skewness4.762916
Sum191445
Variance4900395.7
MonotonicityNot monotonic
2024-05-04T07:47:58.091399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 30
 
11.6%
2 14
 
5.4%
3 12
 
4.6%
4 11
 
4.2%
10 10
 
3.9%
5 9
 
3.5%
6 9
 
3.5%
11 8
 
3.1%
16 5
 
1.9%
9 5
 
1.9%
Other values (112) 146
56.4%
ValueCountFrequency (%)
1 30
11.6%
2 14
5.4%
3 12
 
4.6%
4 11
 
4.2%
5 9
 
3.5%
6 9
 
3.5%
7 2
 
0.8%
8 4
 
1.5%
9 5
 
1.9%
10 10
 
3.9%
ValueCountFrequency (%)
16449 1
0.4%
16301 1
0.4%
15438 1
0.4%
8537 1
0.4%
8240 1
0.4%
7640 1
0.4%
7477 1
0.4%
6129 1
0.4%
5153 1
0.4%
5123 1
0.4%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct259
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3613819 × 108
Minimum5560
Maximum9.7240144 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:47:58.684525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5560
5-th percentile598214
Q18425075
median68495480
Q31.9938202 × 108
95-th percentile4.5920337 × 108
Maximum9.7240144 × 108
Range9.7239588 × 108
Interquartile range (IQR)1.9095694 × 108

Descriptive statistics

Standard deviation1.7630685 × 108
Coefficient of variation (CV)1.295058
Kurtosis5.321292
Mean1.3613819 × 108
Median Absolute Deviation (MAD)65673840
Skewness2.1290127
Sum3.5259792 × 1010
Variance3.1084107 × 1016
MonotonicityNot monotonic
2024-05-04T07:47:59.325499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8608900 1
 
0.4%
241621360 1
 
0.4%
227031470 1
 
0.4%
198869510 1
 
0.4%
261693710 1
 
0.4%
18192880 1
 
0.4%
225992210 1
 
0.4%
808598120 1
 
0.4%
93606830 1
 
0.4%
6516020 1
 
0.4%
Other values (249) 249
96.1%
ValueCountFrequency (%)
5560 1
0.4%
5660 1
0.4%
69160 1
0.4%
80750 1
0.4%
105880 1
0.4%
239500 1
0.4%
304500 1
0.4%
365950 1
0.4%
433470 1
0.4%
454770 1
0.4%
ValueCountFrequency (%)
972401440 1
0.4%
893466760 1
0.4%
820158390 1
0.4%
814934170 1
0.4%
811388550 1
0.4%
808598120 1
0.4%
630057310 1
0.4%
604850970 1
0.4%
570413490 1
0.4%
552666850 1
0.4%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2450.9421
Minimum1
Maximum45296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:47:59.864502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q117
median71
Q3597.5
95-th percentile20563.8
Maximum45296
Range45295
Interquartile range (IQR)580.5

Descriptive statistics

Standard deviation6941.7844
Coefficient of variation (CV)2.8322923
Kurtosis14.808057
Mean2450.9421
Median Absolute Deviation (MAD)66
Skewness3.7057853
Sum634794
Variance48188371
MonotonicityNot monotonic
2024-05-04T07:48:00.432742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 9
 
3.5%
2 7
 
2.7%
9 7
 
2.7%
1 6
 
2.3%
8 5
 
1.9%
10 5
 
1.9%
12 4
 
1.5%
5 4
 
1.5%
4 4
 
1.5%
27 3
 
1.2%
Other values (161) 205
79.2%
ValueCountFrequency (%)
1 6
2.3%
2 7
2.7%
3 9
3.5%
4 4
1.5%
5 4
1.5%
6 3
 
1.2%
7 3
 
1.2%
8 5
1.9%
9 7
2.7%
10 5
1.9%
ValueCountFrequency (%)
45296 1
0.4%
41302 1
0.4%
41006 1
0.4%
28995 1
0.4%
25430 1
0.4%
24463 1
0.4%
24254 1
0.4%
23446 1
0.4%
23242 1
0.4%
22939 1
0.4%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct259
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2264135 × 108
Minimum5660
Maximum4.0526865 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-05-04T07:48:00.915082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5660
5-th percentile2116535
Q142191345
median2.1890627 × 108
Q35.7104513 × 108
95-th percentile1.3893132 × 109
Maximum4.0526865 × 109
Range4.0526808 × 109
Interquartile range (IQR)5.2885378 × 108

Descriptive statistics

Standard deviation6.2106208 × 108
Coefficient of variation (CV)1.4694778
Kurtosis13.204236
Mean4.2264135 × 108
Median Absolute Deviation (MAD)1.9928072 × 108
Skewness3.1955769
Sum1.0946411 × 1011
Variance3.8571811 × 1017
MonotonicityNot monotonic
2024-05-04T07:48:01.473308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22890870 1
 
0.4%
570957690 1
 
0.4%
472353480 1
 
0.4%
483228080 1
 
0.4%
675394760 1
 
0.4%
50587520 1
 
0.4%
1039033760 1
 
0.4%
3880465930 1
 
0.4%
315652310 1
 
0.4%
57193490 1
 
0.4%
Other values (249) 249
96.1%
ValueCountFrequency (%)
5660 1
0.4%
86410 1
0.4%
91970 1
0.4%
192290 1
0.4%
365950 1
0.4%
681490 1
0.4%
841380 1
0.4%
1245970 1
0.4%
1426260 1
0.4%
1551880 1
0.4%
ValueCountFrequency (%)
4052686500 1
0.4%
3897703350 1
0.4%
3880465930 1
0.4%
3159219740 1
0.4%
2667468990 1
0.4%
2652261280 1
0.4%
2339061350 1
0.4%
2123501760 1
0.4%
1786394500 1
0.4%
1591149940 1
0.4%

Interactions

2024-05-04T07:47:49.415794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:43.332012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:44.705281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:46.147289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:47.653020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:49.758989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:43.604260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:44.987596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:46.474622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:48.242710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:50.065781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:43.875979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:45.282633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:46.850166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:48.543159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:50.424053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:44.147804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:45.587737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:47.107847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:48.851543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:50.822718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:44.392370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:45.831214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:47.350112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:47:49.136616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:48:01.867635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.6950.6820.0000.1330.0600.000
세목명0.6951.0000.6280.5410.4470.4830.473
체납액구간0.6820.6281.0000.3610.5440.2460.388
체납건수0.0000.5410.3611.0000.5210.8730.499
체납금액0.1330.4470.5440.5211.0000.5430.931
누적체납건수0.0600.4830.2460.8730.5431.0000.658
누적체납금액0.0000.4730.3880.4990.9310.6581.000
2024-05-04T07:48:02.327374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.249
세목명0.2491.000
2024-05-04T07:48:02.594805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.1370.2300.0990.2250.3970.356
체납건수0.1371.0000.4640.9660.4570.2260.157
체납금액0.2300.4641.0000.3810.9610.2050.238
누적체납건수0.0990.9660.3811.0000.4230.2330.098
누적체납금액0.2250.4570.9610.4231.0000.2200.157
세목명0.3970.2260.2050.2330.2201.0000.249
체납액구간0.3560.1570.2380.0980.1570.2491.000

Missing values

2024-05-04T07:47:51.505900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:47:52.498619image/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
0충청남도아산시442002017등록면허세10만원 미만5818608900159322890870
1충청남도아산시442002017자동차세10만원 미만213410132655011685569514580
2충청남도아산시442002017자동차세10만원~30만원미만2663438136740110181786394500
3충청남도아산시442002017자동차세30만원~50만원미만10836989300371126929580
4충청남도아산시442002017자동차세50만원~1백만원미만632699707346255650
5충청남도아산시442002017재산세10만원 미만2792686565608808236341280
6충청남도아산시442002017재산세10만원~30만원미만365576981701425222888130
7충청남도아산시442002017재산세1백만원~3백만원미만4680839670144250258850
8충청남도아산시442002017재산세1천만원~3천만원미만46042283010158204320
9충청남도아산시442002017재산세30만원~50만원미만511911630017361795380
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
249충청남도아산시442002022취득세10만원~30만원미만1222624607113818620
250충청남도아산시442002022취득세1백만원~3백만원미만305535962088154935460
251충청남도아산시442002022취득세1억원~3억원미만11909219602309261280
252충청남도아산시442002022취득세1천만원~3천만원미만915259884028510288080
253충청남도아산시442002022취득세30만원~50만원미만62422160249789060
254충청남도아산시442002022취득세3백만원~5백만원미만135057606027106029430
255충청남도아산시442002022취득세3천만원~5천만원미만28101206013516334460
256충청남도아산시442002022취득세50만원~1백만원미만18123452406545337300
257충청남도아산시442002022취득세5백만원~1천만원미만1612208443037271843300
258충청남도아산시442002022취득세5천만원~1억원미만156719420141055623220