Overview

Dataset statistics

Number of variables10
Number of observations213
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.0 KiB
Average record size in memory86.6 B

Variable types

Categorical6
Numeric4

Dataset

Description세목별, 체납액 구간별 지방세 체납건수 및 체납금액 데이터를 제공합니다. 지방세 체납 정책 수립시 기초자료로 활용할 수 있습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=350&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079078

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 started2024-01-09 21:33:01.646471
Analysis finished2024-01-09 21:33:03.483703
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
충청남도
213 

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 213
100.0%

Length

2024-01-10T06:33:03.531213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:33:03.602263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 213
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
아산시
213 

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 213
100.0%

Length

2024-01-10T06:33:03.677779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:33:03.748964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아산시 213
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
44200
213 

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 213
100.0%

Length

2024-01-10T06:33:03.823915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:33:03.900710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44200 213
100.0%

과세년도
Categorical

Distinct5
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2021
45 
2020
44 
2018
43 
2019
43 
2017
38 

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 (%)
2021 45
21.1%
2020 44
20.7%
2018 43
20.2%
2019 43
20.2%
2017 38
17.8%

Length

2024-01-10T06:33:03.973641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:33:04.055645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 45
21.1%
2020 44
20.7%
2018 43
20.2%
2019 43
20.2%
2017 38
17.8%

세목명
Categorical

Distinct14
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
지방소득세
32 
취득세
27 
재산세
26 
지방소득세
22 
취득세
21 
Other values (9)
85 

Length

Max length9
Median length7
Mean length4.6525822
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 32
15.0%
취득세 27
12.7%
재산세 26
12.2%
지방소득세 22
10.3%
취득세 21
9.9%
주민세 19
8.9%
재산세 19
8.9%
자동차세 12
 
5.6%
주민세 12
 
5.6%
자동차세 8
 
3.8%
Other values (4) 15
7.0%

Length

2024-01-10T06:33:04.151923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지방소득세 54
25.4%
취득세 48
22.5%
재산세 45
21.1%
주민세 31
14.6%
자동차세 20
 
9.4%
지역자원시설세 8
 
3.8%
등록면허세 7
 
3.3%

체납액구간
Categorical

Distinct22
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
10만원 미만
19 
50만원~1백만원미만
16 
30만원~50만원미만
15 
10만원~30만원미만
15 
10만원 미만
14 
Other values (17)
134 

Length

Max length13
Median length12
Mean length11.117371
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
 
8.9%
50만원~1백만원미만 16
 
7.5%
30만원~50만원미만 15
 
7.0%
10만원~30만원미만 15
 
7.0%
10만원 미만 14
 
6.6%
1백만원~3백만원미만 13
 
6.1%
5백만원~1천만원미만 12
 
5.6%
10만원~30만원미만 12
 
5.6%
3백만원~5백만원미만 10
 
4.7%
30만원~50만원미만 10
 
4.7%
Other values (12) 77
36.2%

Length

2024-01-10T06:33:04.247932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 33
13.4%
미만 33
13.4%
10만원~30만원미만 27
11.0%
50만원~1백만원미만 26
10.6%
30만원~50만원미만 25
10.2%
1백만원~3백만원미만 22
8.9%
5백만원~1천만원미만 18
7.3%
3백만원~5백만원미만 18
7.3%
1천만원~3천만원미만 15
6.1%
3천만원~5천만원미만 13
 
5.3%
Other values (2) 16
6.5%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)49.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean687.15962
Minimum1
Maximum16301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-01-10T06:33:04.343874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median16
Q3189
95-th percentile4754.6
Maximum16301
Range16300
Interquartile range (IQR)185

Descriptive statistics

Standard deviation2058.8387
Coefficient of variation (CV)2.9961579
Kurtosis28.959031
Mean687.15962
Median Absolute Deviation (MAD)15
Skewness4.8645616
Sum146365
Variance4238816.8
MonotonicityNot monotonic
2024-01-10T06:33:04.464980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 26
 
12.2%
2 11
 
5.2%
4 10
 
4.7%
10 10
 
4.7%
3 9
 
4.2%
5 8
 
3.8%
6 8
 
3.8%
11 7
 
3.3%
8 4
 
1.9%
16 3
 
1.4%
Other values (96) 117
54.9%
ValueCountFrequency (%)
1 26
12.2%
2 11
5.2%
3 9
 
4.2%
4 10
 
4.7%
5 8
 
3.8%
6 8
 
3.8%
7 2
 
0.9%
8 4
 
1.9%
9 3
 
1.4%
10 10
 
4.7%
ValueCountFrequency (%)
16301 1
0.5%
15438 1
0.5%
8537 1
0.5%
7640 1
0.5%
7477 1
0.5%
6129 1
0.5%
5153 1
0.5%
5123 1
0.5%
5009 1
0.5%
4879 1
0.5%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct213
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2856598 × 108
Minimum5560
Maximum9.7240144 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-01-10T06:33:04.623501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5560
5-th percentile515960
Q18241250
median62208000
Q31.8886799 × 108
95-th percentile4.4395454 × 108
Maximum9.7240144 × 108
Range9.7239588 × 108
Interquartile range (IQR)1.8062674 × 108

Descriptive statistics

Standard deviation1.7026856 × 108
Coefficient of variation (CV)1.3243672
Kurtosis6.1764353
Mean1.2856598 × 108
Median Absolute Deviation (MAD)59293680
Skewness2.246381
Sum2.7384554 × 1010
Variance2.8991384 × 1016
MonotonicityNot monotonic
2024-01-10T06:33:04.732376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8608900 1
 
0.5%
104359630 1
 
0.5%
417019900 1
 
0.5%
180503420 1
 
0.5%
387900380 1
 
0.5%
352500460 1
 
0.5%
80750 1
 
0.5%
365950 1
 
0.5%
605660 1
 
0.5%
1804200 1
 
0.5%
Other values (203) 203
95.3%
ValueCountFrequency (%)
5560 1
0.5%
5660 1
0.5%
69160 1
0.5%
80750 1
0.5%
239500 1
0.5%
304500 1
0.5%
365950 1
0.5%
433470 1
0.5%
454770 1
0.5%
475430 1
0.5%
ValueCountFrequency (%)
972401440 1
0.5%
893466760 1
0.5%
820158390 1
0.5%
808598120 1
0.5%
604850970 1
0.5%
570413490 1
0.5%
552666850 1
0.5%
537228660 1
0.5%
532351820 1
0.5%
517902510 1
0.5%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2335.1455
Minimum1
Maximum45296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-01-10T06:33:04.836378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q117
median70
Q3530
95-th percentile18324
Maximum45296
Range45295
Interquartile range (IQR)513

Descriptive statistics

Standard deviation6645.1631
Coefficient of variation (CV)2.8457169
Kurtosis16.036024
Mean2335.1455
Median Absolute Deviation (MAD)65
Skewness3.7972597
Sum497386
Variance44158193
MonotonicityNot monotonic
2024-01-10T06:33:04.938321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 8
 
3.8%
1 6
 
2.8%
2 5
 
2.3%
9 5
 
2.3%
4 4
 
1.9%
10 4
 
1.9%
8 4
 
1.9%
12 4
 
1.9%
73 3
 
1.4%
22 3
 
1.4%
Other values (137) 167
78.4%
ValueCountFrequency (%)
1 6
2.8%
2 5
2.3%
3 8
3.8%
4 4
1.9%
5 3
 
1.4%
6 3
 
1.4%
7 3
 
1.4%
8 4
1.9%
9 5
2.3%
10 4
1.9%
ValueCountFrequency (%)
45296 1
0.5%
41006 1
0.5%
28995 1
0.5%
24463 1
0.5%
24254 1
0.5%
23242 1
0.5%
22939 1
0.5%
22398 1
0.5%
21530 1
0.5%
20458 1
0.5%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct213
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9108709 × 108
Minimum5660
Maximum4.0526865 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-01-10T06:33:05.045135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5660
5-th percentile2472814
Q140774070
median1.796317 × 108
Q35.0892775 × 108
95-th percentile1.2967567 × 109
Maximum4.0526865 × 109
Range4.0526808 × 109
Interquartile range (IQR)4.6815368 × 108

Descriptive statistics

Standard deviation5.8971386 × 108
Coefficient of variation (CV)1.5078837
Kurtosis15.043389
Mean3.9108709 × 108
Median Absolute Deviation (MAD)1.641825 × 108
Skewness3.3853011
Sum8.330155 × 1010
Variance3.4776244 × 1017
MonotonicityNot monotonic
2024-01-10T06:33:05.155939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22890870 1
 
0.5%
233377010 1
 
0.5%
1268410630 1
 
0.5%
554959660 1
 
0.5%
1291631330 1
 
0.5%
1229123880 1
 
0.5%
86410 1
 
0.5%
365950 1
 
0.5%
2739870 1
 
0.5%
14610280 1
 
0.5%
Other values (203) 203
95.3%
ValueCountFrequency (%)
5660 1
0.5%
86410 1
0.5%
91970 1
0.5%
365950 1
0.5%
841380 1
0.5%
1245970 1
0.5%
1426260 1
0.5%
1551880 1
0.5%
1679440 1
0.5%
1957460 1
0.5%
ValueCountFrequency (%)
4052686500 1
0.5%
3880465930 1
0.5%
3159219740 1
0.5%
2652261280 1
0.5%
2339061350 1
0.5%
2123501760 1
0.5%
1786394500 1
0.5%
1591149940 1
0.5%
1531140210 1
0.5%
1370686100 1
0.5%

Interactions

2024-01-10T06:33:03.013730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:01.943384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.478115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.753912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:03.092030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.240386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.548408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.822206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:03.160622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.309446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.614416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.883746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:03.228617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.395949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.675555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:33:02.943368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:33:05.234011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.6980.6800.0000.2270.1360.191
세목명0.6981.0000.5840.6110.4620.5530.539
체납액구간0.6800.5841.0000.2600.5870.3450.458
체납건수0.0000.6110.2601.0000.5010.9120.483
체납금액0.2270.4620.5870.5011.0000.5260.943
누적체납건수0.1360.5530.3450.9120.5261.0000.658
누적체납금액0.1910.5390.4580.4830.9430.6581.000
2024-01-10T06:33:05.316582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.4010.446
체납액구간0.4011.0000.222
세목명0.4460.2221.000
2024-01-10T06:33:05.388261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.4590.9640.4790.0000.2670.108
체납금액0.4591.0000.3570.9660.1270.2130.262
누적체납건수0.9640.3571.0000.4220.0820.2770.141
누적체납금액0.4790.9660.4221.0000.1090.2590.190
과세년도0.0000.1270.0820.1091.0000.4460.401
세목명0.2670.2130.2770.2590.4461.0000.222
체납액구간0.1080.2620.1410.1900.4010.2221.000

Missing values

2024-01-10T06:33:03.332618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:33:03.440850image/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
203충청남도아산시442002021취득세10만원 미만18800100672938870
204충청남도아산시442002021취득세10만원~30만원미만1015425407214079650
205충청남도아산시442002021취득세1백만원~3백만원미만173440950077128563730
206충청남도아산시442002021취득세1천만원~3천만원미만1018598203025474516550
207충청남도아산시442002021취득세30만원~50만원미만72550640269940420
208충청남도아산시442002021취득세3백만원~5백만원미만10433379302184963640
209충청남도아산시442002021취득세3천만원~5천만원미만52073355208318643570
210충청남도아산시442002021취득세50만원~1백만원미만1182412506243608620
211충청남도아산시442002021취득세5백만원~1천만원미만139418937032218906270
212충청남도아산시442002021취득세5천만원~1억원미만216149941012903357440