Overview

Dataset statistics

Number of variables10
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.7 KiB
Average record size in memory86.9 B

Variable types

Categorical6
Numeric4

Dataset

Description부산광역시해운대구_지방세체납현황_20201231
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15078945

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 1 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수 and 1 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 started2023-12-10 16:28:58.523185
Analysis finished2023-12-10 16:29:00.450468
Duration1.93 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
부산광역시
150 

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 150
100.0%

Length

2023-12-11T01:29:00.540603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:00.670846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 150
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
해운대구
150 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 150
100.0%

Length

2023-12-11T01:29:00.813413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:00.917007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 150
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
26350
150 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26350
2nd row26350
3rd row26350
4th row26350
5th row26350

Common Values

ValueCountFrequency (%)
26350 150
100.0%

Length

2023-12-11T01:29:01.017886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:01.110627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 150
100.0%

과세년도
Categorical

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2020
47 
2019
39 
2018
33 
2017
31 

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 (%)
2020 47
31.3%
2019 39
26.0%
2018 33
22.0%
2017 31
20.7%

Length

2023-12-11T01:29:01.225012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:01.363547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 47
31.3%
2019 39
26.0%
2018 33
22.0%
2017 31
20.7%

세목명
Categorical

Distinct7
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
지방소득세
35 
재산세
33 
취득세
29 
주민세
22 
자동차세
16 
Other values (2)
15 

Length

Max length7
Median length3
Mean length3.88
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 35
23.3%
재산세 33
22.0%
취득세 29
19.3%
주민세 22
14.7%
자동차세 16
10.7%
지역자원시설세 8
 
5.3%
등록면허세 7
 
4.7%

Length

2023-12-11T01:29:01.481622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:01.610410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 35
23.3%
재산세 33
22.0%
취득세 29
19.3%
주민세 22
14.7%
자동차세 16
10.7%
지역자원시설세 8
 
5.3%
등록면허세 7
 
4.7%

체납액구간
Categorical

Distinct13
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
10만원 미만
28 
30만원~50만원미만
23 
10만원~30만원미만
21 
50만원~1백만원미만
21 
1백만원~3백만원미만
16 
Other values (8)
41 

Length

Max length11
Median length11
Mean length10.173333
Min length7

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row50만원~1백만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 28
18.7%
30만원~50만원미만 23
15.3%
10만원~30만원미만 21
14.0%
50만원~1백만원미만 21
14.0%
1백만원~3백만원미만 16
10.7%
3백만원~5백만원미만 13
8.7%
5백만원~1천만원미만 10
 
6.7%
1천만원~3천만원미만 6
 
4.0%
3천만원~5천만원미만 4
 
2.7%
1억원~3억원미만 3
 
2.0%
Other values (3) 5
 
3.3%

Length

2023-12-11T01:29:01.759087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 28
15.7%
미만 28
15.7%
30만원~50만원미만 23
12.9%
10만원~30만원미만 21
11.8%
50만원~1백만원미만 21
11.8%
1백만원~3백만원미만 16
9.0%
3백만원~5백만원미만 13
7.3%
5백만원~1천만원미만 10
 
5.6%
1천만원~3천만원미만 6
 
3.4%
3천만원~5천만원미만 4
 
2.2%
Other values (4) 8
 
4.5%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758.54667
Minimum1
Maximum18459
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T01:29:01.903690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.25
median14
Q3215.75
95-th percentile4125.45
Maximum18459
Range18458
Interquartile range (IQR)213.5

Descriptive statistics

Standard deviation2356.5673
Coefficient of variation (CV)3.1066873
Kurtosis28.767997
Mean758.54667
Median Absolute Deviation (MAD)13
Skewness4.9862117
Sum113782
Variance5553409.6
MonotonicityNot monotonic
2023-12-11T01:29:02.054604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 29
 
19.3%
3 11
 
7.3%
2 9
 
6.0%
4 8
 
5.3%
5 6
 
4.0%
7 3
 
2.0%
6 2
 
1.3%
22 2
 
1.3%
15 2
 
1.3%
12 2
 
1.3%
Other values (71) 76
50.7%
ValueCountFrequency (%)
1 29
19.3%
2 9
 
6.0%
3 11
 
7.3%
4 8
 
5.3%
5 6
 
4.0%
6 2
 
1.3%
7 3
 
2.0%
9 1
 
0.7%
10 1
 
0.7%
11 2
 
1.3%
ValueCountFrequency (%)
18459 1
0.7%
13201 1
0.7%
11023 1
0.7%
8953 1
0.7%
7346 1
0.7%
4839 1
0.7%
4413 1
0.7%
4287 1
0.7%
3928 1
0.7%
3576 1
0.7%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0291931 × 108
Minimum16190
Maximum8.3844724 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T01:29:02.194073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16190
5-th percentile395858.5
Q13761087.5
median27511185
Q31.0822236 × 108
95-th percentile5.1001597 × 108
Maximum8.3844724 × 108
Range8.3843105 × 108
Interquartile range (IQR)1.0446127 × 108

Descriptive statistics

Standard deviation1.7016438 × 108
Coefficient of variation (CV)1.6533766
Kurtosis6.1319231
Mean1.0291931 × 108
Median Absolute Deviation (MAD)26992190
Skewness2.4652781
Sum1.5437897 × 1010
Variance2.8955916 × 1016
MonotonicityNot monotonic
2023-12-11T01:29:02.344181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22192960 1
 
0.7%
194110 1
 
0.7%
5922880 1
 
0.7%
11682960 1
 
0.7%
400440 1
 
0.7%
3609930 1
 
0.7%
1872710 1
 
0.7%
10635000 1
 
0.7%
759874420 1
 
0.7%
99592900 1
 
0.7%
Other values (140) 140
93.3%
ValueCountFrequency (%)
16190 1
0.7%
22130 1
0.7%
74400 1
0.7%
194110 1
0.7%
223260 1
0.7%
244800 1
0.7%
370800 1
0.7%
392110 1
0.7%
400440 1
0.7%
424020 1
0.7%
ValueCountFrequency (%)
838447240 1
0.7%
776625140 1
0.7%
759874420 1
0.7%
731948280 1
0.7%
622933040 1
0.7%
579097890 1
0.7%
535213580 1
0.7%
529856820 1
0.7%
485766040 1
0.7%
443284690 1
0.7%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2613.7667
Minimum1
Maximum67774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T01:29:02.499219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111.5
median62.5
Q3530.75
95-th percentile14935.25
Maximum67774
Range67773
Interquartile range (IQR)519.25

Descriptive statistics

Standard deviation8363.1074
Coefficient of variation (CV)3.1996381
Kurtosis32.143826
Mean2613.7667
Median Absolute Deviation (MAD)60.5
Skewness5.2100166
Sum392065
Variance69941566
MonotonicityNot monotonic
2023-12-11T01:29:02.636693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
7.3%
3 6
 
4.0%
2 6
 
4.0%
4 5
 
3.3%
18 4
 
2.7%
6 3
 
2.0%
5 3
 
2.0%
19 3
 
2.0%
21 2
 
1.3%
391 2
 
1.3%
Other values (103) 105
70.0%
ValueCountFrequency (%)
1 11
7.3%
2 6
4.0%
3 6
4.0%
4 5
3.3%
5 3
 
2.0%
6 3
 
2.0%
7 1
 
0.7%
8 1
 
0.7%
10 1
 
0.7%
11 1
 
0.7%
ValueCountFrequency (%)
67774 1
0.7%
49315 1
0.7%
36114 1
0.7%
25091 1
0.7%
21503 1
0.7%
19985 1
0.7%
17581 1
0.7%
15572 1
0.7%
14157 1
0.7%
13653 1
0.7%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3589053 × 108
Minimum148210
Maximum3.3771078 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-11T01:29:02.776913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum148210
5-th percentile870925.5
Q120676350
median60038600
Q32.6968875 × 108
95-th percentile8.8557651 × 108
Maximum3.3771078 × 109
Range3.3769596 × 109
Interquartile range (IQR)2.490124 × 108

Descriptive statistics

Standard deviation4.3613814 × 108
Coefficient of variation (CV)1.8489006
Kurtosis23.70615
Mean2.3589053 × 108
Median Absolute Deviation (MAD)54619330
Skewness4.2622713
Sum3.5383579 × 1010
Variance1.9021648 × 1017
MonotonicityNot monotonic
2023-12-11T01:29:02.904550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51259540 1
 
0.7%
6216750 1
 
0.7%
35976940 1
 
0.7%
11682960 1
 
0.7%
5619490 1
 
0.7%
12688160 1
 
0.7%
34080750 1
 
0.7%
10635000 1
 
0.7%
759874420 1
 
0.7%
211338980 1
 
0.7%
Other values (140) 140
93.3%
ValueCountFrequency (%)
148210 1
0.7%
170340 1
0.7%
244740 1
0.7%
392110 1
0.7%
467800 1
0.7%
500580 1
0.7%
759150 1
0.7%
834300 1
0.7%
915690 1
0.7%
1380140 1
0.7%
ValueCountFrequency (%)
3377107820 1
0.7%
2600482680 1
0.7%
1762035440 1
0.7%
1398657000 1
0.7%
1276269400 1
0.7%
1002002430 1
0.7%
968538350 1
0.7%
955372310 1
0.7%
800270530 1
0.7%
790849350 1
0.7%

Interactions

2023-12-11T01:28:59.756599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:58.796048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.132026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.435964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.839082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:58.872958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.209030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.516536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.918234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:58.942056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.270712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.585458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:29:00.034503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.031966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.347187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:28:59.673488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:29:02.997303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.3250.1590.328
세목명0.0001.0000.0000.2940.2030.5270.233
체납액구간0.0000.0001.0000.0000.7180.0000.000
체납건수0.0000.2940.0001.0000.6680.9740.901
체납금액0.3250.2030.7180.6681.0000.6520.831
누적체납건수0.1590.5270.0000.9740.6521.0000.775
누적체납금액0.3280.2330.0000.9010.8310.7751.000
2023-12-11T01:29:03.096572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2023-12-11T01:29:03.454082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.6130.9370.7100.0000.1600.000
체납금액0.6131.0000.4270.9530.1970.1050.395
누적체납건수0.9370.4271.0000.5960.1070.2060.000
누적체납금액0.7100.9530.5961.0000.1500.1270.000
과세년도0.0000.1970.1070.1501.0000.0000.000
세목명0.1600.1050.2060.1270.0001.0000.000
체납액구간0.0000.3950.0000.0000.0000.0001.000

Missing values

2023-12-11T01:29:00.186997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:29:00.381472image/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
0부산광역시해운대구263502017등록면허세10만원 미만62722192960150751259540
1부산광역시해운대구263502017자동차세10만원 미만1595701799308768389726940
2부산광역시해운대구263502017자동차세10만원~30만원미만208434625488078651276269400
3부산광역시해운대구263502017자동차세30만원~50만원미만9432051980294102340520
4부산광역시해운대구263502017자동차세50만원~1백만원미만1161473305031566290
5부산광역시해운대구263502017재산세10만원 미만1942627505207298222404790
6부산광역시해운대구263502017재산세10만원~30만원미만26843951330873137278270
7부산광역시해운대구263502017재산세1백만원~3백만원미만20268248306385352290
8부산광역시해운대구263502017재산세30만원~50만원미만30109048109634833710
9부산광역시해운대구263502017재산세50만원~1백만원미만27180708807047442820
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
140부산광역시해운대구263502020주민세10만원 미만18459443284690677741398657000
141부산광역시해운대구263502020주민세10만원~30만원미만1001568746033254296240
142부산광역시해운대구263502020주민세1백만원~3백만원미만112461503150747050
143부산광역시해운대구263502020주민세30만원~50만원미만1661869904116402990
144부산광역시해운대구263502020주민세3백만원~5백만원미만27336450622944230
145부산광역시해운대구263502020주민세50만원~1백만원미만643888403020646070
146부산광역시해운대구263502020지방소득세10만원 미만2013580736205921174909890
147부산광역시해운대구263502020지방소득세10만원~30만원미만6031114334001763325251270
148부산광역시해운대구263502020지방소득세1백만원~3백만원미만207344425780598968538350
149부산광역시해운대구263502020지방소득세1억원~3억원미만36229330403622933040