Overview

Dataset statistics

Number of variables10
Number of observations69
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory87.9 B

Variable types

Categorical6
Numeric4

Dataset

Description상기 데이터는 연도별 체납액 규모별 체납 건수를 납세자 유형별로 제공하여 체납정책 수립시 기초자료로 활용할 수 있도록 제공함
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=348&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079977

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수 and 1 other fieldsHigh correlation
체납금액 is highly overall correlated with 누적체납금액High correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:16:30.516176
Analysis finished2024-01-09 22:16:32.061030
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
충청남도
69 

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 69
100.0%

Length

2024-01-10T07:16:32.113453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:32.199119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 69
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
부여군
69 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부여군 69
100.0%

Length

2024-01-10T07:16:32.296327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:32.389769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부여군 69
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size684.0 B
44760
69 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44760
2nd row44760
3rd row44760
4th row44760
5th row44760

Common Values

ValueCountFrequency (%)
44760 69
100.0%

Length

2024-01-10T07:16:32.488474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:32.581025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44760 69
100.0%

과세년도
Categorical

Distinct3
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size684.0 B
2019
27 
2018
22 
2017
20 

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 27
39.1%
2018 22
31.9%
2017 20
29.0%

Length

2024-01-10T07:16:32.679520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:32.756929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 27
39.1%
2018 22
31.9%
2017 20
29.0%

세목명
Categorical

Distinct6
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size684.0 B
지방소득세
23 
재산세
20 
자동차세
9 
주민세
9 
취득세
5 

Length

Max length5
Median length4
Mean length3.884058
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 23
33.3%
재산세 20
29.0%
자동차세 9
 
13.0%
주민세 9
 
13.0%
취득세 5
 
7.2%
등록면허세 3
 
4.3%

Length

2024-01-10T07:16:32.856294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:32.950846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 23
33.3%
재산세 20
29.0%
자동차세 9
 
13.0%
주민세 9
 
13.0%
취득세 5
 
7.2%
등록면허세 3
 
4.3%

체납액구간
Categorical

Distinct8
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size684.0 B
10만원 미만
16 
10만원~30만원미만
13 
30만원~50만원미만
12 
50만원~1백만원미만
8 
1백만원~3백만원미만
7 
Other values (3)
13 

Length

Max length11
Median length11
Mean length10.072464
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 16
23.2%
10만원~30만원미만 13
18.8%
30만원~50만원미만 12
17.4%
50만원~1백만원미만 8
11.6%
1백만원~3백만원미만 7
10.1%
3백만원~5백만원미만 6
 
8.7%
5백만원~1천만원미만 5
 
7.2%
1천만원~3천만원미만 2
 
2.9%

Length

2024-01-10T07:16:33.054974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:33.162459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 16
18.8%
미만 16
18.8%
10만원~30만원미만 13
15.3%
30만원~50만원미만 12
14.1%
50만원~1백만원미만 8
9.4%
1백만원~3백만원미만 7
8.2%
3백만원~5백만원미만 6
 
7.1%
5백만원~1천만원미만 5
 
5.9%
1천만원~3천만원미만 2
 
2.4%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.391304
Minimum1
Maximum1330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T07:16:33.269770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q320
95-th percentile650.8
Maximum1330
Range1329
Interquartile range (IQR)18

Descriptive statistics

Standard deviation238.82077
Coefficient of variation (CV)2.5572056
Kurtosis12.79417
Mean93.391304
Median Absolute Deviation (MAD)4
Skewness3.4593181
Sum6444
Variance57035.359
MonotonicityNot monotonic
2024-01-10T07:16:33.371327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
2 15
21.7%
1 14
20.3%
4 3
 
4.3%
9 2
 
2.9%
8 2
 
2.9%
10 2
 
2.9%
20 2
 
2.9%
6 2
 
2.9%
11 2
 
2.9%
3 2
 
2.9%
Other values (23) 23
33.3%
ValueCountFrequency (%)
1 14
20.3%
2 15
21.7%
3 2
 
2.9%
4 3
 
4.3%
5 1
 
1.4%
6 2
 
2.9%
7 1
 
1.4%
8 2
 
2.9%
9 2
 
2.9%
10 2
 
2.9%
ValueCountFrequency (%)
1330 1
1.4%
917 1
1.4%
762 1
1.4%
748 1
1.4%
505 1
1.4%
374 1
1.4%
348 1
1.4%
300 1
1.4%
191 1
1.4%
179 1
1.4%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7274281.7
Minimum108340
Maximum58011950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T07:16:33.473408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum108340
5-th percentile168572
Q1918950
median3651880
Q310029050
95-th percentile22192914
Maximum58011950
Range57903610
Interquartile range (IQR)9110100

Descriptive statistics

Standard deviation9387594.6
Coefficient of variation (CV)1.2905184
Kurtosis11.963198
Mean7274281.7
Median Absolute Deviation (MAD)3199770
Skewness2.8667951
Sum5.0192544 × 108
Variance8.8126933 × 1013
MonotonicityNot monotonic
2024-01-10T07:16:33.576844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
514040 1
 
1.4%
58011950 1
 
1.4%
6710010 1
 
1.4%
2765260 1
 
1.4%
9083200 1
 
1.4%
9134100 1
 
1.4%
23151150 1
 
1.4%
3189240 1
 
1.4%
13374350 1
 
1.4%
11079310 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
108340 1
1.4%
113300 1
1.4%
139380 1
1.4%
139420 1
1.4%
212300 1
1.4%
349380 1
1.4%
430660 1
1.4%
452110 1
1.4%
496870 1
1.4%
514040 1
1.4%
ValueCountFrequency (%)
58011950 1
1.4%
31667040 1
1.4%
23151150 1
1.4%
22891570 1
1.4%
21144930 1
1.4%
21068170 1
1.4%
20562410 1
1.4%
19896930 1
1.4%
16642940 1
1.4%
14553530 1
1.4%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.02899
Minimum1
Maximum4675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T07:16:33.676415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.4
Q15
median18
Q371
95-th percentile1882.2
Maximum4675
Range4674
Interquartile range (IQR)66

Descriptive statistics

Standard deviation809.34276
Coefficient of variation (CV)2.6021458
Kurtosis14.942748
Mean311.02899
Median Absolute Deviation (MAD)15
Skewness3.676154
Sum21461
Variance655035.71
MonotonicityNot monotonic
2024-01-10T07:16:33.990891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3 5
 
7.2%
2 4
 
5.8%
1 4
 
5.8%
9 3
 
4.3%
4 3
 
4.3%
5 3
 
4.3%
6 3
 
4.3%
18 3
 
4.3%
10 2
 
2.9%
7 2
 
2.9%
Other values (34) 37
53.6%
ValueCountFrequency (%)
1 4
5.8%
2 4
5.8%
3 5
7.2%
4 3
4.3%
5 3
4.3%
6 3
4.3%
7 2
 
2.9%
8 1
 
1.4%
9 3
4.3%
10 2
 
2.9%
ValueCountFrequency (%)
4675 1
1.4%
3345 1
1.4%
2428 1
1.4%
2187 1
1.4%
1425 1
1.4%
1136 1
1.4%
1099 1
1.4%
920 1
1.4%
836 1
1.4%
751 1
1.4%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct69
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21036354
Minimum349380
Maximum1.8383106 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size753.0 B
2024-01-10T07:16:34.096384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum349380
5-th percentile908590
Q13098510
median11054180
Q328000790
95-th percentile74832706
Maximum1.8383106 × 108
Range1.8348168 × 108
Interquartile range (IQR)24902280

Descriptive statistics

Standard deviation30682066
Coefficient of variation (CV)1.4585258
Kurtosis12.711872
Mean21036354
Median Absolute Deviation (MAD)9447460
Skewness3.1860776
Sum1.4515084 × 109
Variance9.4138916 × 1014
MonotonicityNot monotonic
2024-01-10T07:16:34.206624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
906590 1
 
1.4%
183831060 1
 
1.4%
19826160 1
 
1.4%
8480700 1
 
1.4%
39689600 1
 
1.4%
20598140 1
 
1.4%
73424980 1
 
1.4%
7241190 1
 
1.4%
48361020 1
 
1.4%
17047320 1
 
1.4%
Other values (59) 59
85.5%
ValueCountFrequency (%)
349380 1
1.4%
426630 1
1.4%
846250 1
1.4%
906590 1
1.4%
911590 1
1.4%
915920 1
1.4%
1055300 1
1.4%
1144320 1
1.4%
1168600 1
1.4%
1228370 1
1.4%
ValueCountFrequency (%)
183831060 1
1.4%
125819110 1
1.4%
94152070 1
1.4%
75771190 1
1.4%
73424980 1
1.4%
54703020 1
1.4%
50273830 1
1.4%
48361020 1
1.4%
39689600 1
1.4%
36457350 1
1.4%

Interactions

2024-01-10T07:16:31.578266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:30.793684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.065224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.330979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.644208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:30.859839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.135216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.393153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.718574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:30.925072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.199410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.457561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.800440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:30.989907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.260202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:31.511658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:16:34.284436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.0000.2280.000
세목명0.0001.0000.0000.4470.3090.4630.000
체납액구간0.0000.0001.0000.0000.4760.0000.225
체납건수0.0000.4470.0001.0000.5600.9800.772
체납금액0.0000.3090.4760.5601.0000.6110.892
누적체납건수0.2280.4630.0000.9800.6111.0000.894
누적체납금액0.0000.0000.2250.7720.8920.8941.000
2024-01-10T07:16:34.371034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2024-01-10T07:16:34.444677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.4530.9490.5020.0000.2810.000
체납금액0.4531.0000.2980.9220.0000.1250.281
누적체납건수0.9490.2981.0000.4410.1370.2720.000
누적체납금액0.5020.9220.4411.0000.0000.0000.083
과세년도0.0000.0000.1370.0001.0000.0000.000
세목명0.2810.1250.2720.0000.0001.0000.000
체납액구간0.0000.2810.0000.0830.0000.0001.000

Missing values

2024-01-10T07:16:31.903280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:16:32.014990image/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
0충청남도부여군447602017등록면허세10만원 미만3651404073906590
1충청남도부여군447602017자동차세10만원 미만146588288065727613550
2충청남도부여군447602017자동차세10만원~30만원미만1382289157056094152070
3충청남도부여군447602017자동차세30만원~50만원미만2626460103443540
4충청남도부여군447602017재산세10만원 미만74810470460242836457350
5충청남도부여군447602017재산세10만원~30만원미만111951940508522680
6충청남도부여군447602017재산세1백만원~3백만원미만22137100913963460
7충청남도부여군447602017재산세30만원~50만원미만264295082751810
8충청남도부여군447602017재산세3백만원~5백만원미만2613697026136970
9충청남도부여군447602017재산세50만원~1백만원미만211207401611538100
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
59충청남도부여군447602019지방소득세10만원~30만원미만2036518805811340190
60충청남도부여군447602019지방소득세1백만원~3백만원미만10210681703975771190
61충청남도부여군447602019지방소득세1천만원~3천만원미만110029050231173980
62충청남도부여군447602019지방소득세30만원~50만원미만62436210124991160
63충청남도부여군447602019지방소득세3백만원~5백만원미만26805780934806570
64충청남도부여군447602019지방소득세50만원~1백만원미만1286152803021703670
65충청남도부여군447602019지방소득세5백만원~1천만원미만214553530533282540
66충청남도부여군447602019취득세10만원 미만21083409426630
67충청남도부여군447602019취득세30만원~50만원미만14968702846250
68충청남도부여군447602019취득세50만원~1백만원미만151976031720800