Overview

Dataset statistics

Number of variables11
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory96.2 B

Variable types

Categorical7
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
데이터기준일자 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수High correlation
체납금액 is highly overall correlated with 누적체납금액High correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액High correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2024-01-09 22:16:39.880888
Analysis finished2024-01-09 22:16:41.390386
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
충청남도
60 

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 60
100.0%

Length

2024-01-10T07:16:41.438865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:41.513146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 60
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
부여군
60 

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 60
100.0%

Length

2024-01-10T07:16:41.587285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:41.655500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부여군 60
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
44760
60 

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 60
100.0%

Length

2024-01-10T07:16:41.728708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:41.797211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44760 60
100.0%

과세년도
Categorical

Distinct2
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
2020
30 
2021
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 30
50.0%
2021 30
50.0%

Length

2024-01-10T07:16:41.872795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:41.949868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 30
50.0%
2021 30
50.0%

세목명
Categorical

Distinct6
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
지방소득세
18 
재산세
14 
취득세
12 
주민세
8 
자동차세
6 

Length

Max length5
Median length3
Mean length3.7666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 18
30.0%
재산세 14
23.3%
취득세 12
20.0%
주민세 8
13.3%
자동차세 6
 
10.0%
등록면허세 2
 
3.3%

Length

2024-01-10T07:16:42.035061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:42.129660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 18
30.0%
재산세 14
23.3%
취득세 12
20.0%
주민세 8
13.3%
자동차세 6
 
10.0%
등록면허세 2
 
3.3%

체납액구간
Categorical

Distinct9
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
10만원 미만
12 
10만원~30만원미만
10 
30만원~50만원미만
9 
50만원~1백만원미만
8 
1백만원~3백만원미만
6 
Other values (4)
15 

Length

Max length11
Median length11
Mean length10.133333
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 12
20.0%
10만원~30만원미만 10
16.7%
30만원~50만원미만 9
15.0%
50만원~1백만원미만 8
13.3%
1백만원~3백만원미만 6
10.0%
3백만원~5백만원미만 5
8.3%
1천만원~3천만원미만 4
 
6.7%
5백만원~1천만원미만 4
 
6.7%
1억원~3억원미만 2
 
3.3%

Length

2024-01-10T07:16:42.231641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:42.336816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 12
16.7%
미만 12
16.7%
10만원~30만원미만 10
13.9%
30만원~50만원미만 9
12.5%
50만원~1백만원미만 8
11.1%
1백만원~3백만원미만 6
8.3%
3백만원~5백만원미만 5
6.9%
1천만원~3천만원미만 4
 
5.6%
5백만원~1천만원미만 4
 
5.6%
1억원~3억원미만 2
 
2.8%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.46667
Minimum1
Maximum3352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-01-10T07:16:42.451398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median9
Q332
95-th percentile2009.3
Maximum3352
Range3351
Interquartile range (IQR)30

Descriptive statistics

Standard deviation685.93167
Coefficient of variation (CV)2.7386146
Kurtosis11.983249
Mean250.46667
Median Absolute Deviation (MAD)8
Skewness3.4878192
Sum15028
Variance470502.25
MonotonicityNot monotonic
2024-01-10T07:16:42.549670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 10
16.7%
2 8
 
13.3%
4 4
 
6.7%
3 4
 
6.7%
32 2
 
3.3%
29 2
 
3.3%
25 2
 
3.3%
23 2
 
3.3%
12 2
 
3.3%
9 2
 
3.3%
Other values (22) 22
36.7%
ValueCountFrequency (%)
1 10
16.7%
2 8
13.3%
3 4
 
6.7%
4 4
 
6.7%
5 1
 
1.7%
6 1
 
1.7%
8 1
 
1.7%
9 2
 
3.3%
11 1
 
1.7%
12 2
 
3.3%
ValueCountFrequency (%)
3352 1
1.7%
3077 1
1.7%
2129 1
1.7%
2003 1
1.7%
888 1
1.7%
713 1
1.7%
606 1
1.7%
528 1
1.7%
314 1
1.7%
269 1
1.7%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22213255
Minimum83130
Maximum1.6897179 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-01-10T07:16:42.654358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83130
5-th percentile351288
Q13149295
median9277775
Q331990800
95-th percentile92160543
Maximum1.6897179 × 108
Range1.6888866 × 108
Interquartile range (IQR)28841505

Descriptive statistics

Standard deviation31598808
Coefficient of variation (CV)1.4225204
Kurtosis7.9105891
Mean22213255
Median Absolute Deviation (MAD)7688200
Skewness2.5718258
Sum1.3327953 × 109
Variance9.9848465 × 1014
MonotonicityNot monotonic
2024-01-10T07:16:42.767196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4306270 1
 
1.7%
35945970 1
 
1.7%
11163920 1
 
1.7%
74641700 1
 
1.7%
51899570 1
 
1.7%
40280710 1
 
1.7%
10710430 1
 
1.7%
8231140 1
 
1.7%
17837100 1
 
1.7%
33605250 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
83130 1
1.7%
207220 1
1.7%
299570 1
1.7%
354010 1
1.7%
368500 1
1.7%
396370 1
1.7%
917480 1
1.7%
1493390 1
1.7%
1573960 1
1.7%
1605190 1
1.7%
ValueCountFrequency (%)
168971790 1
1.7%
104608470 1
1.7%
100409830 1
1.7%
91726370 1
1.7%
74641700 1
1.7%
67138100 1
1.7%
51899570 1
1.7%
47324490 1
1.7%
44673380 1
1.7%
40280710 1
1.7%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean575.66667
Minimum1
Maximum8219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-01-10T07:16:42.878550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median20
Q384
95-th percentile4004.4
Maximum8219
Range8218
Interquartile range (IQR)79

Descriptive statistics

Standard deviation1625.2328
Coefficient of variation (CV)2.8232185
Kurtosis14.079985
Mean575.66667
Median Absolute Deviation (MAD)19
Skewness3.7057404
Sum34540
Variance2641381.6
MonotonicityNot monotonic
2024-01-10T07:16:42.979666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 6
 
10.0%
5 5
 
8.3%
4 4
 
6.7%
9 3
 
5.0%
16 3
 
5.0%
48 2
 
3.3%
8 2
 
3.3%
6 2
 
3.3%
461 1
 
1.7%
10 1
 
1.7%
Other values (31) 31
51.7%
ValueCountFrequency (%)
1 6
10.0%
3 1
 
1.7%
4 4
6.7%
5 5
8.3%
6 2
 
3.3%
7 1
 
1.7%
8 2
 
3.3%
9 3
5.0%
10 1
 
1.7%
12 1
 
1.7%
ValueCountFrequency (%)
8219 1
1.7%
7752 1
1.7%
4316 1
1.7%
3988 1
1.7%
1965 1
1.7%
1849 1
1.7%
1640 1
1.7%
1627 1
1.7%
486 1
1.7%
461 1
1.7%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44727366
Minimum375760
Maximum2.7801498 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2024-01-10T07:16:43.082001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum375760
5-th percentile1202873
Q15939395
median18331550
Q366031028
95-th percentile1.4676129 × 108
Maximum2.7801498 × 108
Range2.7763922 × 108
Interquartile range (IQR)60091632

Descriptive statistics

Standard deviation58932781
Coefficient of variation (CV)1.3176001
Kurtosis6.6832838
Mean44727366
Median Absolute Deviation (MAD)15665195
Skewness2.3831199
Sum2.683642 × 109
Variance3.4730726 × 1015
MonotonicityNot monotonic
2024-01-10T07:16:43.195600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7122970 1
 
1.7%
81272920 1
 
1.7%
19840800 1
 
1.7%
145592320 1
 
1.7%
73643470 1
 
1.7%
68698000 1
 
1.7%
17900640 1
 
1.7%
15369490 1
 
1.7%
31679610 1
 
1.7%
61056300 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
375760 1
1.7%
633850 1
1.7%
1045420 1
1.7%
1211160 1
1.7%
1215970 1
1.7%
1512820 1
1.7%
2638280 1
1.7%
2694430 1
1.7%
2773790 1
1.7%
3005530 1
1.7%
ValueCountFrequency (%)
278014980 1
1.7%
275557430 1
1.7%
168971790 1
1.7%
145592320 1
1.7%
140563080 1
1.7%
100409830 1
1.7%
100221480 1
1.7%
93941290 1
1.7%
83047360 1
1.7%
81272920 1
1.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2022-08-31
60 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-31
2nd row2022-08-31
3rd row2022-08-31
4th row2022-08-31
5th row2022-08-31

Common Values

ValueCountFrequency (%)
2022-08-31 60
100.0%

Length

2024-01-10T07:16:43.299109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:43.372190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-08-31 60
100.0%

Interactions

2024-01-10T07:16:40.941643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.142255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.417084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.686484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:41.008243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.211358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.488925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.751793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:41.077946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.286346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.558353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.820716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:41.135707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.351942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.618847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:40.877446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:16:43.419837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.7360.1490.7360.412
체납액구간0.0000.0001.0000.0000.5120.0000.614
체납건수0.0000.7360.0001.0000.7381.0000.795
체납금액0.0000.1490.5120.7381.0000.7380.883
누적체납건수0.0000.7360.0001.0000.7381.0000.795
누적체납금액0.0000.4120.6140.7950.8830.7951.000
2024-01-10T07:16:43.731007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2024-01-10T07:16:43.799568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.3930.9720.4360.0000.3460.000
체납금액0.3931.0000.3440.9620.0000.0910.272
누적체납건수0.9720.3441.0000.4240.0000.3460.000
누적체납금액0.4360.9620.4241.0000.0000.2610.371
과세년도0.0000.0000.0000.0001.0000.0000.000
세목명0.3460.0910.3460.2610.0001.0000.000
체납액구간0.0000.2720.0000.3710.0000.0001.000

Missing values

2024-01-10T07:16:41.227977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:16:41.345048image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자
0충청남도부여군447602020등록면허세10만원 미만269430627046171229702022-08-31
1충청남도부여군447602020자동차세10만원 미만713292593301849776203502022-08-31
2충청남도부여군447602020자동차세10만원~30만원미만5289172637016272755574302022-08-31
3충청남도부여군447602020자동차세30만원~50만원미만25846276047157039502022-08-31
4충청남도부여군447602020재산세10만원 미만30776713810077521405630802022-08-31
5충청남도부여군447602020재산세10만원~30만원미만26444673380385652715202022-08-31
6충청남도부여군447602020재산세1백만원~3백만원미만193573433041754239302022-08-31
7충청남도부여군447602020재산세1천만원~3천만원미만1187292401187292402022-08-31
8충청남도부여군447602020재산세30만원~50만원미만25945316048179338602022-08-31
9충청남도부여군447602020재산세3백만원~5백만원미만261807308260068902022-08-31
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자
50충청남도부여군447602021지방소득세3백만원~5백만원미만31304909016616027202022-08-31
51충청남도부여군447602021지방소득세50만원~1백만원미만12839261040291011102022-08-31
52충청남도부여군447602021지방소득세5백만원~1천만원미만4284578409641995602022-08-31
53충청남도부여군447602021취득세10만원 미만28313093757602022-08-31
54충청남도부여군447602021취득세10만원~30만원미만2354010510454202022-08-31
55충청남도부여군447602021취득세1백만원~3백만원미만591023909155505802022-08-31
56충청남도부여군447602021취득세1천만원~3천만원미만1117876701117876702022-08-31
57충청남도부여군447602021취득세30만원~50만원미만1396370312159702022-08-31
58충청남도부여군447602021취득세50만원~1백만원미만21493390426944302022-08-31
59충청남도부여군447602021취득세5백만원~1천만원미만15729920157299202022-08-31