Overview

Dataset statistics

Number of variables9
Number of observations27
Missing cells6
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory82.9 B

Variable types

Categorical5
Numeric4

Dataset

Description부산광역시해운대구_지방세비과감면율현황_20201231
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15078931

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 2 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 비과세금액 and 1 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세감면율High correlation
비과세금액 has 3 (11.1%) missing valuesMissing
부과금액 has 1 (3.7%) missing valuesMissing
비과세감면율 has 2 (7.4%) missing valuesMissing
감면금액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:16:00.814848
Analysis finished2023-12-10 16:16:04.036158
Duration3.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
부산광역시
27 

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 27
100.0%

Length

2023-12-11T01:16:04.136848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:16:04.282726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 27
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
해운대구
27 

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 27
100.0%

Length

2023-12-11T01:16:04.443211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:16:04.933632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 27
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
26350
27 

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 27
100.0%

Length

2023-12-11T01:16:05.087045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:16:05.233883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 27
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size348.0 B
재산세
4 
주민세
4 
취득세
4 
자동차세
4 
등록면허세
4 
Other values (3)
7 

Length

Max length7
Median length3
Mean length4.037037
Min length3

Unique

Unique1 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
재산세 4
14.8%
주민세 4
14.8%
취득세 4
14.8%
자동차세 4
14.8%
등록면허세 4
14.8%
지역자원시설세 4
14.8%
등록세 2
7.4%
교육세 1
 
3.7%

Length

2023-12-11T01:16:05.406392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:16:05.611054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 4
14.8%
주민세 4
14.8%
취득세 4
14.8%
자동차세 4
14.8%
등록면허세 4
14.8%
지역자원시설세 4
14.8%
등록세 2
7.4%
교육세 1
 
3.7%

과세년도
Categorical

Distinct4
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size348.0 B
2018
7 
2019
7 
2020
7 
2017
6 

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 (%)
2018 7
25.9%
2019 7
25.9%
2020 7
25.9%
2017 6
22.2%

Length

2023-12-11T01:16:05.827807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:16:05.996858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 7
25.9%
2019 7
25.9%
2020 7
25.9%
2017 6
22.2%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing3
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean7.389924 × 109
Minimum860360
Maximum4.3530476 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:16:06.155730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum860360
5-th percentile91051800
Q12.31006 × 108
median7.40862 × 108
Q34.5178781 × 109
95-th percentile3.8321702 × 1010
Maximum4.3530476 × 1010
Range4.3529616 × 1010
Interquartile range (IQR)4.2868721 × 109

Descriptive statistics

Standard deviation1.3832044 × 1010
Coefficient of variation (CV)1.8717438
Kurtosis2.1174798
Mean7.389924 × 109
Median Absolute Deviation (MAD)6.2186225 × 108
Skewness1.9101952
Sum1.7735818 × 1011
Variance1.9132545 × 1020
MonotonicityNot monotonic
2023-12-11T01:16:06.357829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
916765000 1
 
3.7%
860360 1
 
3.7%
127275500 1
 
3.7%
231980670 1
 
3.7%
3582472160 1
 
3.7%
589751790 1
 
3.7%
43530476240 1
 
3.7%
742927000 1
 
3.7%
110724000 1
 
3.7%
228082000 1
 
3.7%
Other values (14) 14
51.9%
(Missing) 3
 
11.1%
ValueCountFrequency (%)
860360 1
3.7%
89895000 1
3.7%
97607000 1
3.7%
110724000 1
3.7%
127275500 1
3.7%
228082000 1
3.7%
231980670 1
3.7%
502331000 1
3.7%
589751790 1
3.7%
594046000 1
3.7%
ValueCountFrequency (%)
43530476240 1
3.7%
38946532000 1
3.7%
34781002000 1
3.7%
30820057000 1
3.7%
7564455000 1
3.7%
7324096000 1
3.7%
3582472160 1
3.7%
3302206000 1
3.7%
926387000 1
3.7%
916765000 1
3.7%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8747697 × 109
Minimum1000
Maximum2.527997 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:16:06.677466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile4908008
Q12.165055 × 108
median5.2131 × 108
Q35.1667485 × 109
95-th percentile1.5657236 × 1010
Maximum2.527997 × 1010
Range2.5279969 × 1010
Interquartile range (IQR)4.950243 × 109

Descriptive statistics

Standard deviation6.2956898 × 109
Coefficient of variation (CV)1.6247907
Kurtosis4.5429922
Mean3.8747697 × 109
Median Absolute Deviation (MAD)5.21309 × 108
Skewness2.1774516
Sum1.0461878 × 1011
Variance3.9635711 × 1019
MonotonicityNot monotonic
2023-12-11T01:16:06.892936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4843943000 1
 
3.7%
521310000 1
 
3.7%
894440 1
 
3.7%
173753050 1
 
3.7%
1694908080 1
 
3.7%
13336779890 1
 
3.7%
416820810 1
 
3.7%
5699090780 1
 
3.7%
3091830130 1
 
3.7%
218962000 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1000 1
3.7%
894440 1
3.7%
14273000 1
3.7%
162293000 1
3.7%
173753050 1
3.7%
200366000 1
3.7%
214049000 1
3.7%
218962000 1
3.7%
325232000 1
3.7%
348596000 1
3.7%
ValueCountFrequency (%)
25279970000 1
3.7%
15968644000 1
3.7%
14930617000 1
3.7%
13336779890 1
3.7%
5740960000 1
3.7%
5699090780 1
3.7%
5489554000 1
3.7%
4843943000 1
3.7%
3091830130 1
3.7%
1850944000 1
3.7%

부과금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing1
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean6.2715371 × 1010
Minimum70099320
Maximum4.46342 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:16:07.082085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70099320
5-th percentile5.0640742 × 109
Q11.3362729 × 1010
median3.0645887 × 1010
Q39.2451022 × 1010
95-th percentile1.482805 × 1011
Maximum4.46342 × 1011
Range4.462719 × 1011
Interquartile range (IQR)7.9088293 × 1010

Descriptive statistics

Standard deviation9.1482931 × 1010
Coefficient of variation (CV)1.4587003
Kurtosis12.502107
Mean6.2715371 × 1010
Median Absolute Deviation (MAD)1.9845412 × 1010
Skewness3.1999548
Sum1.6305996 × 1012
Variance8.3691267 × 1021
MonotonicityNot monotonic
2023-12-11T01:16:07.269618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
85642850000 1
 
3.7%
10463821000 1
 
3.7%
70099320 1
 
3.7%
28039494740 1
 
3.7%
43899714980 1
 
3.7%
446342000000 1
 
3.7%
11137129760 1
 
3.7%
149490000000 1
 
3.7%
3760258990 1
 
3.7%
15821521000 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
70099320 1
3.7%
3760258990 1
3.7%
8975520000 1
3.7%
9573594000 1
3.7%
10463821000 1
3.7%
11137129760 1
3.7%
13223527000 1
3.7%
13780335000 1
3.7%
15641788000 1
3.7%
15821521000 1
3.7%
ValueCountFrequency (%)
446342000000 1
3.7%
149490000000 1
3.7%
144651984000 1
3.7%
128546786000 1
3.7%
126878338000 1
3.7%
102611502000 1
3.7%
94720413000 1
3.7%
85642850000 1
3.7%
43899714980 1
3.7%
37017918000 1
3.7%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)100.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean16.3052
Minimum1.07
Maximum82.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-11T01:16:07.490265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.07
5-th percentile1.428
Q14.39
median6.56
Q318.55
95-th percentile43.344
Maximum82.22
Range81.15
Interquartile range (IQR)14.16

Descriptive statistics

Standard deviation19.088335
Coefficient of variation (CV)1.17069
Kurtosis4.9381635
Mean16.3052
Median Absolute Deviation (MAD)5.27
Skewness2.0862587
Sum407.63
Variance364.36454
MonotonicityNot monotonic
2023-12-11T01:16:07.705827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
15.93 1
 
3.7%
2.5 1
 
3.7%
1.07 1
 
3.7%
4.39 1
 
3.7%
3.79 1
 
3.7%
9.04 1
 
3.7%
32.93 1
 
3.7%
82.22 1
 
3.7%
6.08 1
 
3.7%
1.98 1
 
3.7%
Other values (15) 15
55.6%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
1.07 1
3.7%
1.29 1
3.7%
1.98 1
3.7%
2.3 1
3.7%
2.5 1
3.7%
3.79 1
3.7%
4.39 1
3.7%
6.08 1
3.7%
6.18 1
3.7%
6.23 1
3.7%
ValueCountFrequency (%)
82.22 1
3.7%
43.55 1
3.7%
42.52 1
3.7%
41.64 1
3.7%
32.93 1
3.7%
22.23 1
3.7%
18.55 1
3.7%
15.93 1
3.7%
15.39 1
3.7%
14.98 1
3.7%

Interactions

2023-12-11T01:16:02.939258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:01.245820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:01.884155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:02.447102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:03.094547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:01.389183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:02.033041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:02.567111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:03.228217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:01.532178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:02.175678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:02.702440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:03.347681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:01.688270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:02.304587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:16:02.808031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:16:07.859314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.5660.6840.3350.924
과세년도0.0001.0000.0000.0000.0780.000
비과세금액0.5660.0001.0000.7430.8920.832
감면금액0.6840.0000.7431.0000.7800.843
부과금액0.3350.0780.8920.7801.0000.832
비과세감면율0.9240.0000.8320.8430.8321.000
2023-12-11T01:16:08.014121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-11T01:16:08.145550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.7810.5960.8730.4170.000
감면금액0.7811.0000.6750.7020.4310.000
부과금액0.5960.6751.0000.2230.1970.000
비과세감면율0.8730.7020.2231.0000.5810.000
세목명0.4170.4310.1970.5811.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2023-12-11T01:16:03.537386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:16:03.740932image/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.
2023-12-11T01:16:03.918820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
0부산광역시해운대구26350재산세20173082005700048439430008564285000041.64
1부산광역시해운대구26350주민세2017908237000521310000897552000015.93
2부산광역시해운대구26350취득세201773240960001493061700014465198400015.39
3부산광역시해운대구26350자동차세20175023310001569585000332522800006.23
4부산광역시해운대구26350등록면허세201789895000214049000132235270002.3
5부산광역시해운대구26350지역자원시설세2017701213000325232000156417880006.56
6부산광역시해운대구26350등록세2018<NA>14273000<NA><NA>
7부산광역시해운대구26350재산세20183478100200054895540009472041300042.52
8부산광역시해운대구26350주민세2018926387000508014000957359400014.98
9부산광역시해운대구26350취득세201833022060002527997000012854678600022.23
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
17부산광역시해운대구26350자동차세20192280820001850944000336229580006.18
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