Overview

Dataset statistics

Number of variables10
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory88.0 B

Variable types

Categorical6
Numeric4

Dataset

Description지방세 체납액 규모별 체납 건수를 납세자 유형별로 제공하여지방세 체납액 징수를 위한 정책 수립시 기초자료로 활용 가능합니다.
Author부산광역시 사상구
URLhttps://www.data.go.kr/data/15079735/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 체납금액 and 2 other fieldsHigh correlation
체납금액 is highly overall correlated with 체납건수 and 2 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수 and 2 other fieldsHigh correlation
누적체납금액 is highly overall correlated with 체납건수 and 2 other fieldsHigh correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2024-04-21 01:52:54.697408
Analysis finished2024-04-21 01:52:58.426048
Duration3.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
부산광역시
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 67
100.0%

Length

2024-04-21T10:52:58.495873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:52:58.605447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 67
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
사상구
67 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사상구 67
100.0%

Length

2024-04-21T10:52:58.725768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:52:58.831456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사상구 67
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
26530
67 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26530
2nd row26530
3rd row26530
4th row26530
5th row26530

Common Values

ValueCountFrequency (%)
26530 67
100.0%

Length

2024-04-21T10:52:58.949852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:52:59.057371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26530 67
100.0%

과세년도
Categorical

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2020
35 
2021
32 

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 35
52.2%
2021 32
47.8%

Length

2024-04-21T10:52:59.171736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:52:59.277971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 35
52.2%
2021 32
47.8%

세목명
Categorical

Distinct7
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size668.0 B
지방소득세
18 
재산세
15 
주민세
11 
취득세
10 
자동차세
8 
Other values (2)
5 

Length

Max length7
Median length3
Mean length3.8955224
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 18
26.9%
재산세 15
22.4%
주민세 11
16.4%
취득세 10
14.9%
자동차세 8
11.9%
지역자원시설세 3
 
4.5%
등록면허세 2
 
3.0%

Length

2024-04-21T10:52:59.402915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:52:59.550668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 18
26.9%
재산세 15
22.4%
주민세 11
16.4%
취득세 10
14.9%
자동차세 8
11.9%
지역자원시설세 3
 
4.5%
등록면허세 2
 
3.0%

체납액구간
Categorical

Distinct9
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size668.0 B
10만원 미만
14 
10만원~30만원미만
10 
30만원~50만원미만
10 
50만원~1백만원미만
10 
1백만원~3백만원미만
8 
Other values (4)
15 

Length

Max length11
Median length11
Mean length10.134328
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 14
20.9%
10만원~30만원미만 10
14.9%
30만원~50만원미만 10
14.9%
50만원~1백만원미만 10
14.9%
1백만원~3백만원미만 8
11.9%
3백만원~5백만원미만 5
 
7.5%
5백만원~1천만원미만 5
 
7.5%
1천만원~3천만원미만 3
 
4.5%
5천만원~1억원미만 2
 
3.0%

Length

2024-04-21T10:52:59.678452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:52:59.804189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 14
17.3%
미만 14
17.3%
10만원~30만원미만 10
12.3%
30만원~50만원미만 10
12.3%
50만원~1백만원미만 10
12.3%
1백만원~3백만원미만 8
9.9%
3백만원~5백만원미만 5
 
6.2%
5백만원~1천만원미만 5
 
6.2%
1천만원~3천만원미만 3
 
3.7%
5천만원~1억원미만 2
 
2.5%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean714.02985
Minimum1
Maximum11770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-04-21T10:52:59.949488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median16
Q3114
95-th percentile3651.6
Maximum11770
Range11769
Interquartile range (IQR)110

Descriptive statistics

Standard deviation2094.4984
Coefficient of variation (CV)2.9333486
Kurtosis19.891438
Mean714.02985
Median Absolute Deviation (MAD)15
Skewness4.3273172
Sum47840
Variance4386923.7
MonotonicityNot monotonic
2024-04-21T10:53:00.139541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 7
 
10.4%
2 6
 
9.0%
6 4
 
6.0%
4 4
 
6.0%
7 3
 
4.5%
9 2
 
3.0%
5 2
 
3.0%
16 2
 
3.0%
3 2
 
3.0%
11047 1
 
1.5%
Other values (34) 34
50.7%
ValueCountFrequency (%)
1 7
10.4%
2 6
9.0%
3 2
 
3.0%
4 4
6.0%
5 2
 
3.0%
6 4
6.0%
7 3
4.5%
9 2
 
3.0%
11 1
 
1.5%
14 1
 
1.5%
ValueCountFrequency (%)
11770 1
1.5%
11047 1
1.5%
4422 1
1.5%
4209 1
1.5%
2351 1
1.5%
2307 1
1.5%
2278 1
1.5%
1894 1
1.5%
1398 1
1.5%
1189 1
1.5%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61925002
Minimum45850
Maximum4.0090336 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-04-21T10:53:00.478194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45850
5-th percentile376075
Q14003560
median33551810
Q368909435
95-th percentile2.6024601 × 108
Maximum4.0090336 × 108
Range4.0085751 × 108
Interquartile range (IQR)64905875

Descriptive statistics

Standard deviation85545820
Coefficient of variation (CV)1.3814423
Kurtosis4.5170405
Mean61925002
Median Absolute Deviation (MAD)31135680
Skewness2.153873
Sum4.1489752 × 109
Variance7.3180873 × 1015
MonotonicityNot monotonic
2024-04-21T10:53:00.646409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18707670 1
 
1.5%
35997630 1
 
1.5%
15954240 1
 
1.5%
271287880 1
 
1.5%
18059590 1
 
1.5%
33563200 1
 
1.5%
26208250 1
 
1.5%
25941620 1
 
1.5%
54162610 1
 
1.5%
95060420 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
45850 1
1.5%
61800 1
1.5%
329690 1
1.5%
361630 1
1.5%
409780 1
1.5%
414540 1
1.5%
554760 1
1.5%
575630 1
1.5%
739610 1
1.5%
1022260 1
1.5%
ValueCountFrequency (%)
400903360 1
1.5%
327998110 1
1.5%
271824590 1
1.5%
271287880 1
1.5%
234481660 1
1.5%
220154770 1
1.5%
213697930 1
1.5%
173239580 1
1.5%
165530760 1
1.5%
118511630 1
1.5%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)71.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1837.1343
Minimum1
Maximum36513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-04-21T10:53:00.819705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median43
Q3211
95-th percentile7259.7
Maximum36513
Range36512
Interquartile range (IQR)203

Descriptive statistics

Standard deviation6264.9484
Coefficient of variation (CV)3.4101744
Kurtosis25.438522
Mean1837.1343
Median Absolute Deviation (MAD)41
Skewness4.9795361
Sum123088
Variance39249579
MonotonicityNot monotonic
2024-04-21T10:53:00.968551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
8 5
 
7.5%
6 4
 
6.0%
1 3
 
4.5%
7 3
 
4.5%
2 3
 
4.5%
30 2
 
3.0%
202 2
 
3.0%
65 2
 
3.0%
23 2
 
3.0%
17 2
 
3.0%
Other values (38) 39
58.2%
ValueCountFrequency (%)
1 3
4.5%
2 3
4.5%
3 2
 
3.0%
6 4
6.0%
7 3
4.5%
8 5
7.5%
10 1
 
1.5%
11 1
 
1.5%
12 1
 
1.5%
13 1
 
1.5%
ValueCountFrequency (%)
36513 1
1.5%
34955 1
1.5%
8558 1
1.5%
8097 1
1.5%
5306 1
1.5%
5268 1
1.5%
4839 1
1.5%
4589 1
1.5%
2189 1
1.5%
2096 1
1.5%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1652101 × 108
Minimum52760
Maximum8.9662014 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-04-21T10:53:01.105878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52760
5-th percentile2599061
Q111258640
median50102590
Q396568030
95-th percentile6.3148067 × 108
Maximum8.9662014 × 108
Range8.9656738 × 108
Interquartile range (IQR)85309390

Descriptive statistics

Standard deviation1.9294363 × 108
Coefficient of variation (CV)1.6558699
Kurtosis7.3664207
Mean1.1652101 × 108
Median Absolute Deviation (MAD)42738260
Skewness2.7538228
Sum7.8069075 × 109
Variance3.7227244 × 1016
MonotonicityNot monotonic
2024-04-21T10:53:01.243754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52071200 1
 
1.5%
35997630 1
 
1.5%
28529140 1
 
1.5%
750195250 1
 
1.5%
18059590 1
 
1.5%
44236820 1
 
1.5%
30267400 1
 
1.5%
37473500 1
 
1.5%
80355070 1
 
1.5%
225959780 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
52760 1
1.5%
64100 1
1.5%
361630 1
1.5%
2563880 1
1.5%
2681150 1
1.5%
2849970 1
1.5%
3351110 1
1.5%
4055820 1
1.5%
4372680 1
1.5%
5267680 1
1.5%
ValueCountFrequency (%)
896620140 1
1.5%
773180630 1
1.5%
750195250 1
1.5%
728622580 1
1.5%
404816200 1
1.5%
401587940 1
1.5%
312302550 1
1.5%
268740050 1
1.5%
225959780 1
1.5%
216399540 1
1.5%

Interactions

2024-04-21T10:52:57.777226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:56.497466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.045055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.415102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.858788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:56.641905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.135985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.510129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.953332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:56.866284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.230665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.606274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:58.054719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:56.957479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.324132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:52:57.696021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:53:01.328906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.0000.4420.5000.5460.399
체납액구간0.0000.0001.0000.2780.2370.1120.000
체납건수0.0000.4420.2781.0000.9840.9980.881
체납금액0.0000.5000.2370.9841.0000.9900.925
누적체납건수0.0000.5460.1120.9980.9901.0000.883
누적체납금액0.0000.3990.0000.8810.9250.8831.000
2024-04-21T10:53:01.434561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간과세년도세목명
체납액구간1.0000.0000.000
과세년도0.0001.0000.000
세목명0.0000.0001.000
2024-04-21T10:53:01.525685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.6590.9420.7610.0000.3090.168
체납금액0.6591.0000.5280.9690.0000.2960.104
누적체납건수0.9420.5281.0000.6720.0000.3970.054
누적체납금액0.7610.9690.6721.0000.0000.1510.000
과세년도0.0000.0000.0000.0001.0000.0000.000
세목명0.3090.2960.3970.1510.0001.0000.000
체납액구간0.1680.1040.0540.0000.0000.0001.000

Missing values

2024-04-21T10:52:58.189472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:52:58.349128image/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
0부산광역시사상구265302020등록면허세10만원 미만52618707670156952071200
1부산광역시사상구265302020자동차세10만원 미만2278950604205306225959780
2부산광역시사상구265302020자동차세10만원~30만원미만18943279981105268896620140
3부산광역시사상구265302020자동차세30만원~50만원미만792762879022075955200
4부산광역시사상구265302020자동차세50만원~1백만원미만21022260137088780
5부산광역시사상구265302020재산세10만원 미만42092344816608097401587940
6부산광역시사상구265302020재산세10만원~30만원미만13982136979302034312302550
7부산광역시사상구265302020재산세1백만원~3백만원미만40687143205393173790
8부산광역시사상구265302020재산세30만원~50만원미만873424293013854529720
9부산광역시사상구265302020재산세3백만원~5백만원미만726927380831398740
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
57부산광역시사상구265302021지방소득세30만원~50만원미만1054054604020280075780
58부산광역시사상구265302021지방소득세3백만원~5백만원미만14520015302386771680
59부산광역시사상구265302021지방소득세50만원~1백만원미만9669104550202141718370
60부산광역시사상구265302021지방소득세5백만원~1천만원미만9647817901070075380
61부산광역시사상구265302021지방소득세5천만원~1억원미만169312880169312880
62부산광역시사상구265302021지역자원시설세10만원 미만645850852760
63부산광역시사상구265302021취득세10만원 미만9414540622681150
64부산광역시사상구265302021취득세10만원~30만원미만4739610305440720
65부산광역시사상구265302021취득세1백만원~3백만원미만24210740814019880
66부산광역시사상구265302021취득세50만원~1백만원미만425360501712087600