Overview

Dataset statistics

Number of variables10
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory88.8 B

Variable types

Categorical6
Numeric4

Dataset

Description지방세에 대한 체납액 규모별 체납 건수를 납세자 유형별로 제공합니다.(체납액 구간, 체납 건수, 체납 금액, 누적 체납 건수, 누적 체납 금액 등 )
Author전북특별자치도 익산시
URLhttps://www.data.go.kr/data/15079938/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
과세년도 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 started2024-03-14 09:43:16.462896
Analysis finished2024-03-14 09:43:19.946467
Duration3.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size488.0 B
전북특별자치도
45 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북특별자치도 45
100.0%

Length

2024-03-14T18:43:20.051487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:43:20.221590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 45
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size488.0 B
익산시
45 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
익산시 45
100.0%

Length

2024-03-14T18:43:20.447839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:43:20.653759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
익산시 45
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size488.0 B
45140
45 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45140
2nd row45140
3rd row45140
4th row45140
5th row45140

Common Values

ValueCountFrequency (%)
45140 45
100.0%

Length

2024-03-14T18:43:20.829206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:43:20.996924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
45140 45
100.0%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size488.0 B
2022
45 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 45
100.0%

Length

2024-03-14T18:43:21.170476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:43:21.338934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 45
100.0%

세목명
Categorical

Distinct7
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size488.0 B
지방소득세
11 
재산세
9 
취득세
9 
주민세
6 
등록면허세
4 
Other values (2)
6 

Length

Max length7
Median length3
Mean length3.9333333
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 11
24.4%
재산세 9
20.0%
취득세 9
20.0%
주민세 6
13.3%
등록면허세 4
 
8.9%
자동차세 4
 
8.9%
지역자원시설세 2
 
4.4%

Length

2024-03-14T18:43:21.548891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:43:22.057208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 11
24.4%
재산세 9
20.0%
취득세 9
20.0%
주민세 6
13.3%
등록면허세 4
 
8.9%
자동차세 4
 
8.9%
지역자원시설세 2
 
4.4%

체납액구간
Categorical

Distinct11
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size488.0 B
10만원 미만
7 
10만원~30만원미만
7 
30만원~50만원미만
6 
1백만원~3백만원미만
5 
50만원~1백만원미만
5 
Other values (6)
15 

Length

Max length11
Median length11
Mean length10.311111
Min length7

Unique

Unique2 ?
Unique (%)4.4%

Sample

1st row10만원 미만
2nd row10만원~30만원미만
3rd row1백만원~3백만원미만
4th row30만원~50만원미만
5th row10만원 미만

Common Values

ValueCountFrequency (%)
10만원 미만 7
15.6%
10만원~30만원미만 7
15.6%
30만원~50만원미만 6
13.3%
1백만원~3백만원미만 5
11.1%
50만원~1백만원미만 5
11.1%
1천만원~3천만원미만 4
8.9%
3백만원~5백만원미만 3
6.7%
3천만원~5천만원미만 3
6.7%
5백만원~1천만원미만 3
6.7%
1억원~3억원미만 1
 
2.2%

Length

2024-03-14T18:43:22.446593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 7
13.5%
미만 7
13.5%
10만원~30만원미만 7
13.5%
30만원~50만원미만 6
11.5%
1백만원~3백만원미만 5
9.6%
50만원~1백만원미만 5
9.6%
1천만원~3천만원미만 4
7.7%
3백만원~5백만원미만 3
5.8%
3천만원~5천만원미만 3
5.8%
5백만원~1천만원미만 3
5.8%
Other values (2) 2
 
3.8%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean663.64444
Minimum1
Maximum11408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-14T18:43:22.866539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median19
Q3150
95-th percentile3339.2
Maximum11408
Range11407
Interquartile range (IQR)144

Descriptive statistics

Standard deviation2027.8112
Coefficient of variation (CV)3.0555687
Kurtosis19.484495
Mean663.64444
Median Absolute Deviation (MAD)17
Skewness4.2374738
Sum29864
Variance4112018.2
MonotonicityNot monotonic
2024-03-14T18:43:23.245352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
2 5
 
11.1%
1 4
 
8.9%
6 2
 
4.4%
16 2
 
4.4%
8 2
 
4.4%
121 2
 
4.4%
10 2
 
4.4%
150 2
 
4.4%
29 1
 
2.2%
957 1
 
2.2%
Other values (22) 22
48.9%
ValueCountFrequency (%)
1 4
8.9%
2 5
11.1%
4 1
 
2.2%
6 2
 
4.4%
7 1
 
2.2%
8 2
 
4.4%
10 2
 
4.4%
11 1
 
2.2%
12 1
 
2.2%
15 1
 
2.2%
ValueCountFrequency (%)
11408 1
2.2%
6673 1
2.2%
3361 1
2.2%
3252 1
2.2%
1424 1
2.2%
988 1
2.2%
957 1
2.2%
339 1
2.2%
186 1
2.2%
165 1
2.2%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0989093 × 108
Minimum100570
Maximum6.0177176 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-14T18:43:23.473758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100570
5-th percentile455568
Q111022170
median62435070
Q31.6476829 × 108
95-th percentile4.3004063 × 108
Maximum6.0177176 × 108
Range6.0167119 × 108
Interquartile range (IQR)1.5374612 × 108

Descriptive statistics

Standard deviation1.4436786 × 108
Coefficient of variation (CV)1.3137378
Kurtosis4.389283
Mean1.0989093 × 108
Median Absolute Deviation (MAD)56901830
Skewness2.0689122
Sum4.9450918 × 109
Variance2.084208 × 1016
MonotonicityNot monotonic
2024-03-14T18:43:23.720048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
17098000 1
 
2.2%
488200 1
 
2.2%
200483050 1
 
2.2%
305412170 1
 
2.2%
461197740 1
 
2.2%
59224830 1
 
2.2%
113713390 1
 
2.2%
75588290 1
 
2.2%
98313730 1
 
2.2%
260480660 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
100570 1
2.2%
327000 1
2.2%
447410 1
2.2%
488200 1
2.2%
981610 1
2.2%
1677480 1
2.2%
2211080 1
2.2%
2929470 1
2.2%
3488420 1
2.2%
5533240 1
2.2%
ValueCountFrequency (%)
601771760 1
2.2%
578964670 1
2.2%
461197740 1
2.2%
305412170 1
2.2%
260480660 1
2.2%
239915930 1
2.2%
204024410 1
2.2%
201134720 1
2.2%
200483050 1
2.2%
192794500 1
2.2%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2377.1778
Minimum2
Maximum35716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-14T18:43:23.977391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.2
Q114
median67
Q3354
95-th percentile18791.8
Maximum35716
Range35714
Interquartile range (IQR)340

Descriptive statistics

Standard deviation7012.9783
Coefficient of variation (CV)2.9501278
Kurtosis12.951368
Mean2377.1778
Median Absolute Deviation (MAD)63
Skewness3.5582913
Sum106973
Variance49181865
MonotonicityNot monotonic
2024-03-14T18:43:24.403081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2 3
 
6.7%
60 3
 
6.7%
13 2
 
4.4%
3 2
 
4.4%
469 1
 
2.2%
85 1
 
2.2%
331 1
 
2.2%
128 1
 
2.2%
11 1
 
2.2%
505 1
 
2.2%
Other values (29) 29
64.4%
ValueCountFrequency (%)
2 3
6.7%
3 2
4.4%
4 1
 
2.2%
5 1
 
2.2%
7 1
 
2.2%
11 1
 
2.2%
13 2
4.4%
14 1
 
2.2%
21 1
 
2.2%
23 1
 
2.2%
ValueCountFrequency (%)
35716 1
2.2%
21852 1
2.2%
19451 1
2.2%
16155 1
2.2%
3179 1
2.2%
2696 1
2.2%
2426 1
2.2%
970 1
2.2%
752 1
2.2%
505 1
2.2%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3929562 × 108
Minimum369360
Maximum3.2412648 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size533.0 B
2024-03-14T18:43:24.811208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum369360
5-th percentile886594
Q145381350
median1.333448 × 108
Q34.909353 × 108
95-th percentile9.0488971 × 108
Maximum3.2412648 × 109
Range3.2408954 × 109
Interquartile range (IQR)4.4555395 × 108

Descriptive statistics

Standard deviation5.3945432 × 108
Coefficient of variation (CV)1.5899242
Kurtosis19.143198
Mean3.3929562 × 108
Median Absolute Deviation (MAD)1.2720266 × 108
Skewness3.8753478
Sum1.5268303 × 1010
Variance2.9101096 × 1017
MonotonicityNot monotonic
2024-03-14T18:43:25.251963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
47287590 1
 
2.2%
852050 1
 
2.2%
796710750 1
 
2.2%
492111880 1
 
2.2%
1417779870 1
 
2.2%
133344800 1
 
2.2%
484745130 1
 
2.2%
387182570 1
 
2.2%
363204360 1
 
2.2%
758686180 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
369360 1
2.2%
804780 1
2.2%
852050 1
2.2%
1024770 1
2.2%
2937850 1
2.2%
6554010 1
2.2%
8190600 1
2.2%
13836970 1
2.2%
16467540 1
2.2%
36113860 1
2.2%
ValueCountFrequency (%)
3241264810 1
2.2%
1417779870 1
2.2%
931934450 1
2.2%
796710750 1
2.2%
758686180 1
2.2%
721358480 1
2.2%
628957450 1
2.2%
552249090 1
2.2%
539877490 1
2.2%
522937910 1
2.2%

Interactions

2024-03-14T18:43:18.785003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:16.814378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:17.463279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:18.168172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:18.952251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:17.011531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:17.604390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:18.314747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:19.135736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:17.161264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:17.771174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:18.470627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:19.385747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:17.311547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:17.926252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:43:18.625599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:43:25.524468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간체납건수체납금액누적체납건수누적체납금액
세목명1.0000.0000.4130.1750.3420.419
체납액구간0.0001.0000.0000.7250.0000.000
체납건수0.4130.0001.0000.4530.9900.744
체납금액0.1750.7250.4531.0000.6120.868
누적체납건수0.3420.0000.9900.6121.0000.879
누적체납금액0.4190.0000.7440.8680.8791.000
2024-03-14T18:43:25.790783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간세목명
체납액구간1.0000.000
세목명0.0001.000
2024-03-14T18:43:26.036631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액세목명체납액구간
체납건수1.0000.4590.9730.4570.2670.000
체납금액0.4591.0000.4100.9680.1050.432
누적체납건수0.9730.4101.0000.4370.2140.000
누적체납금액0.4570.9680.4371.0000.2810.000
세목명0.2670.1050.2140.2811.0000.000
체납액구간0.0000.4320.0000.0000.0001.000

Missing values

2024-03-14T18:43:19.595292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:43:19.848346image/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
0전북특별자치도익산시451402022등록면허세10만원 미만98817098000269647287590
1전북특별자치도익산시451402022등록면허세10만원~30만원미만11005702369360
2전북특별자치도익산시451402022등록면허세1백만원~3백만원미만1167748022937850
3전북특별자치도익산시451402022등록면허세30만원~50만원미만14474102804780
4전북특별자치도익산시451402022자동차세10만원 미만325213735878016155721358480
5전북특별자치도익산시451402022자동차세10만원~30만원미만3361578964670194513241264810
6전북특별자치도익산시451402022자동차세30만원~50만원미만18665161950752260547460
7전북특별자치도익산시451402022자동차세50만원~1백만원미만422110806036113860
8전북특별자치도익산시451402022재산세10만원 미만667319032824021852539877490
9전북특별자치도익산시451402022재산세10만원~30만원미만14242399159303179522937910
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
35전북특별자치도익산시451402022지역자원시설세10만원~30만원미만232700051024770
36전북특별자치도익산시451402022취득세10만원 미만169816101636554010
37전북특별자치도익산시451402022취득세10만원~30만원미만1934884207613836970
38전북특별자치도익산시451402022취득세1백만원~3백만원미만10158642804878960550
39전북특별자치도익산시451402022취득세1천만원~3천만원미만24587716013222298100
40전북특별자치도익산시451402022취득세30만원~50만원미만72929470218190600
41전북특별자치도익산시451402022취득세3백만원~5백만원미만6254176602385943750
42전북특별자치도익산시451402022취득세3천만원~5천만원미만2857805803121235910
43전북특별자치도익산시451402022취득세50만원~1백만원미만16110221706042160310
44전북특별자치도익산시451402022취득세5백만원~1천만원미만8546165501496685680