Overview

Dataset statistics

Number of variables10
Number of observations190
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.1 KiB
Average record size in memory86.7 B

Variable types

Categorical5
Numeric5

Dataset

Description부산광역시 동래구의 지방세 체납현황(2017년~2022년)에 대한 데이터로 자치단체코드, 과세년도, 세목명, 체납액구간, 체납건수, 체납금액, 누적체납건수, 누적체납금액 등에 대한 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15087156/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 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 started2023-12-11 23:20:45.110340
Analysis finished2023-12-11 23:20:47.710975
Duration2.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
부산광역시
190 

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 190
100.0%

Length

2023-12-12T08:20:47.775739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:20:47.869986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 190
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
동래구
190 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동래구 190
100.0%

Length

2023-12-12T08:20:47.952955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:20:48.023130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동래구 190
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
26260
190 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26260
2nd row26260
3rd row26260
4th row26260
5th row26260

Common Values

ValueCountFrequency (%)
26260 190
100.0%

Length

2023-12-12T08:20:48.099488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:20:48.176163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26260 190
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.7737
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T08:20:48.241767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6828868
Coefficient of variation (CV)0.00083320564
Kurtosis-1.2031929
Mean2019.7737
Median Absolute Deviation (MAD)1
Skewness-0.17656907
Sum383757
Variance2.832108
MonotonicityIncreasing
2023-12-12T08:20:48.325622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2022 40
21.1%
2021 34
17.9%
2020 33
17.4%
2019 32
16.8%
2018 28
14.7%
2017 23
12.1%
ValueCountFrequency (%)
2017 23
12.1%
2018 28
14.7%
2019 32
16.8%
2020 33
17.4%
2021 34
17.9%
2022 40
21.1%
ValueCountFrequency (%)
2022 40
21.1%
2021 34
17.9%
2020 33
17.4%
2019 32
16.8%
2018 28
14.7%
2017 23
12.1%

세목명
Categorical

Distinct7
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
지방소득세
48 
재산세
46 
취득세
38 
자동차세
24 
주민세
20 
Other values (2)
14 

Length

Max length7
Median length3
Mean length3.8421053
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 48
25.3%
재산세 46
24.2%
취득세 38
20.0%
자동차세 24
12.6%
주민세 20
10.5%
등록면허세 8
 
4.2%
지역자원시설세 6
 
3.2%

Length

2023-12-12T08:20:48.428382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:20:48.528229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 48
25.3%
재산세 46
24.2%
취득세 38
20.0%
자동차세 24
12.6%
주민세 20
10.5%
등록면허세 8
 
4.2%
지역자원시설세 6
 
3.2%

체납액구간
Categorical

Distinct12
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
10만원 미만
42 
10만원~30만원미만
31 
30만원~50만원미만
28 
50만원~1백만원미만
27 
1백만원~3백만원미만
16 
Other values (7)
46 

Length

Max length11
Median length11
Mean length10.057895
Min length7

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 42
22.1%
10만원~30만원미만 31
16.3%
30만원~50만원미만 28
14.7%
50만원~1백만원미만 27
14.2%
1백만원~3백만원미만 16
 
8.4%
3백만원~5백만원미만 13
 
6.8%
5백만원~1천만원미만 13
 
6.8%
1천만원~3천만원미만 8
 
4.2%
3천만원~5천만원미만 5
 
2.6%
1억원~3억원미만 3
 
1.6%
Other values (2) 4
 
2.1%

Length

2023-12-12T08:20:48.639387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 42
18.1%
미만 42
18.1%
10만원~30만원미만 31
13.4%
30만원~50만원미만 28
12.1%
50만원~1백만원미만 27
11.6%
1백만원~3백만원미만 16
 
6.9%
3백만원~5백만원미만 13
 
5.6%
5백만원~1천만원미만 13
 
5.6%
1천만원~3천만원미만 8
 
3.4%
3천만원~5천만원미만 5
 
2.2%
Other values (3) 7
 
3.0%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct101
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean539.16842
Minimum1
Maximum11652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T08:20:48.747262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median20.5
Q3183.75
95-th percentile2235.7
Maximum11652
Range11651
Interquartile range (IQR)180.75

Descriptive statistics

Standard deviation1659.1187
Coefficient of variation (CV)3.0771809
Kurtosis29.603495
Mean539.16842
Median Absolute Deviation (MAD)19.5
Skewness5.1726399
Sum102442
Variance2752675
MonotonicityNot monotonic
2023-12-12T08:20:48.859262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 26
 
13.7%
2 15
 
7.9%
3 13
 
6.8%
5 9
 
4.7%
4 5
 
2.6%
7 4
 
2.1%
9 4
 
2.1%
6 4
 
2.1%
121 3
 
1.6%
12 3
 
1.6%
Other values (91) 104
54.7%
ValueCountFrequency (%)
1 26
13.7%
2 15
7.9%
3 13
6.8%
4 5
 
2.6%
5 9
 
4.7%
6 4
 
2.1%
7 4
 
2.1%
8 2
 
1.1%
9 4
 
2.1%
10 2
 
1.1%
ValueCountFrequency (%)
11652 1
0.5%
11400 1
0.5%
11231 1
0.5%
7069 1
0.5%
6139 1
0.5%
3685 1
0.5%
3374 1
0.5%
2780 1
0.5%
2561 1
0.5%
2332 1
0.5%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63548885
Minimum48400
Maximum4.9363624 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T08:20:48.969658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48400
5-th percentile342071
Q13413467.5
median27883110
Q373503120
95-th percentile2.5420918 × 108
Maximum4.9363624 × 108
Range4.9358784 × 108
Interquartile range (IQR)70089652

Descriptive statistics

Standard deviation91783016
Coefficient of variation (CV)1.4442899
Kurtosis5.0389154
Mean63548885
Median Absolute Deviation (MAD)26232645
Skewness2.2063466
Sum1.2074288 × 1010
Variance8.4241219 × 1015
MonotonicityNot monotonic
2023-12-12T08:20:49.088899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9635200 1
 
0.5%
1797300 1
 
0.5%
241052050 1
 
0.5%
72051660 1
 
0.5%
76524860 1
 
0.5%
66762680 1
 
0.5%
29582830 1
 
0.5%
45663670 1
 
0.5%
70140310 1
 
0.5%
239745120 1
 
0.5%
Other values (180) 180
94.7%
ValueCountFrequency (%)
48400 1
0.5%
70800 1
0.5%
102370 1
0.5%
111240 1
0.5%
132900 1
0.5%
138370 1
0.5%
195200 1
0.5%
300390 1
0.5%
304110 1
0.5%
320120 1
0.5%
ValueCountFrequency (%)
493636240 1
0.5%
416681990 1
0.5%
402806350 1
0.5%
362286080 1
0.5%
362013010 1
0.5%
344069790 1
0.5%
312136480 1
0.5%
309459820 1
0.5%
272490940 1
0.5%
263723640 1
0.5%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1691.7368
Minimum1
Maximum35059
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T08:20:49.424941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q112
median58.5
Q3450.25
95-th percentile9168.8
Maximum35059
Range35058
Interquartile range (IQR)438.25

Descriptive statistics

Standard deviation5085.0236
Coefficient of variation (CV)3.0058006
Kurtosis26.866747
Mean1691.7368
Median Absolute Deviation (MAD)54.5
Skewness4.8931859
Sum321430
Variance25857465
MonotonicityNot monotonic
2023-12-12T08:20:49.539007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
7.9%
9 5
 
2.6%
12 5
 
2.6%
14 4
 
2.1%
10 4
 
2.1%
2 4
 
2.1%
4 3
 
1.6%
5 3
 
1.6%
20 3
 
1.6%
8 3
 
1.6%
Other values (120) 141
74.2%
ValueCountFrequency (%)
1 15
7.9%
2 4
 
2.1%
3 2
 
1.1%
4 3
 
1.6%
5 3
 
1.6%
6 2
 
1.1%
7 2
 
1.1%
8 3
 
1.6%
9 5
 
2.6%
10 4
 
2.1%
ValueCountFrequency (%)
35059 1
0.5%
34511 1
0.5%
33051 1
0.5%
23280 1
0.5%
16211 1
0.5%
10072 1
0.5%
10060 1
0.5%
9910 1
0.5%
9585 1
0.5%
9185 1
0.5%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct190
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5346149 × 108
Minimum79300
Maximum1.704045 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T08:20:49.648051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79300
5-th percentile2240366.5
Q115685772
median55342270
Q31.7597744 × 108
95-th percentile5.8932404 × 108
Maximum1.704045 × 109
Range1.7039657 × 109
Interquartile range (IQR)1.6029167 × 108

Descriptive statistics

Standard deviation2.6468235 × 108
Coefficient of variation (CV)1.7247477
Kurtosis16.989764
Mean1.5346149 × 108
Median Absolute Deviation (MAD)49882750
Skewness3.722229
Sum2.9157683 × 1010
Variance7.0056746 × 1016
MonotonicityNot monotonic
2023-12-12T08:20:49.765596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21725980 1
 
0.5%
3451060 1
 
0.5%
467361900 1
 
0.5%
132709350 1
 
0.5%
114883750 1
 
0.5%
113732090 1
 
0.5%
43493920 1
 
0.5%
89303780 1
 
0.5%
77951760 1
 
0.5%
685249450 1
 
0.5%
Other values (180) 180
94.7%
ValueCountFrequency (%)
79300 1
0.5%
111240 1
0.5%
274500 1
0.5%
412870 1
0.5%
463500 1
0.5%
732990 1
0.5%
957580 1
0.5%
971730 1
0.5%
2061400 1
0.5%
2194300 1
0.5%
ValueCountFrequency (%)
1704045040 1
0.5%
1684766260 1
0.5%
1613224980 1
0.5%
1250938900 1
0.5%
848132550 1
0.5%
685249450 1
0.5%
668118580 1
0.5%
666975250 1
0.5%
663452150 1
0.5%
600518750 1
0.5%

Interactions

2023-12-12T08:20:47.116298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:45.388583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:45.846211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.310798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.706622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:47.219847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:45.479517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:45.926534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.396049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.790885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:47.304272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:45.583972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.046168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.469625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.868284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:47.381258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:45.673715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.124664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.539423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.947397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:47.457287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:45.756246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.213187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:46.617974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:20:47.027553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:20:49.843075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.3190.0170.000
세목명0.0001.0000.2730.5550.3090.4450.441
체납액구간0.0000.2731.0000.0000.6630.0000.283
체납건수0.0000.5550.0001.0000.6870.9350.856
체납금액0.3190.3090.6630.6871.0000.6300.836
누적체납건수0.0170.4450.0000.9350.6301.0000.648
누적체납금액0.0000.4410.2830.8560.8360.6481.000
2023-12-12T08:20:49.923476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간
세목명1.0000.133
체납액구간0.1331.000
2023-12-12T08:20:49.993929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.0120.333-0.0470.2850.0000.000
체납건수0.0121.0000.5550.9390.6530.2210.000
체납금액0.3330.5551.0000.4020.9580.1620.350
누적체납건수-0.0470.9390.4021.0000.5640.2830.000
누적체납금액0.2850.6530.9580.5641.0000.1670.138
세목명0.0000.2210.1620.2830.1671.0000.133
체납액구간0.0000.0000.3500.0000.1380.1331.000

Missing values

2023-12-12T08:20:47.557773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:20:47.665412image/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
0부산광역시동래구262602017등록면허세10만원 미만277963520064421725980
1부산광역시동래구262602017자동차세10만원 미만922402301404457183549470
2부산광역시동래구262602017자동차세10만원~30만원미만10971784961303549575641610
3부산광역시동래구262602017자동차세30만원~50만원미만28937378010435931440
4부산광역시동래구262602017자동차세50만원~1백만원미만31636820116792850
5부산광역시동래구262602017재산세10만원 미만69028594250249796306950
6부산광역시동래구262602017재산세10만원~30만원미만1762705818045268043670
7부산광역시동래구262602017재산세1백만원~3백만원미만224007701221665010
8부산광역시동래구262602017재산세30만원~50만원미만934693803211891380
9부산광역시동래구262602017재산세50만원~1백만원미만1170763502314949440
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
180부산광역시동래구262602022지역자원시설세10만원 미만3349872064971730
181부산광역시동래구262602022취득세10만원 미만7304110562312750
182부산광역시동래구262602022취득세10만원~30만원미만5884040345657940
183부산광역시동래구262602022취득세1억원~3억원미만12300892601230089260
184부산광역시동래구262602022취득세1천만원~3천만원미만229112820229112820
185부산광역시동래구262602022취득세30만원~50만원미만62638060145587690
186부산광역시동래구262602022취득세3백만원~5백만원미만1314887013148870
187부산광역시동래구262602022취득세3억원~5억원미만14936362401493636240
188부산광역시동래구262602022취득세50만원~1백만원미만741460602516853710
189부산광역시동래구262602022취득세5백만원~1천만원미만427887600533547890